From d4d6e85bcb98b13f71a7432cd1e05897fe36056d Mon Sep 17 00:00:00 2001 From: Dylan Cashman Date: Sat, 12 Oct 2024 23:07:49 -0400 Subject: [PATCH] Final program update sat before conference --- program/calendar.html | 4 +- program/event_a-scivis-contest.html | 2 +- program/event_a-vast-challenge.html | 2 +- program/event_s-vds.html | 2 +- program/event_v-full.html | 2 +- program/event_w-eduvis.html | 2 +- program/paper_a-scivis-contest-1.html | 127 ++++++++++++++++++++++++++ program/paper_a-scivis-contest-2.html | 127 ++++++++++++++++++++++++++ program/paper_a-scivis-contest-3.html | 127 ++++++++++++++++++++++++++ program/paper_v-full-1026.html | 2 +- program/paper_v-full-1031.html | 2 +- program/paper_v-full-1032.html | 2 +- program/paper_v-full-1039.html | 2 +- program/paper_v-full-1059.html | 2 +- program/paper_v-full-1060.html | 2 +- program/paper_v-full-1063.html | 2 +- program/paper_v-full-1067.html | 2 +- program/paper_v-full-1077.html | 2 +- program/paper_v-full-1096.html | 2 +- program/paper_v-full-1099.html | 2 +- program/paper_v-full-1100.html | 2 +- program/paper_v-full-1121.html | 2 +- program/paper_v-full-1128.html | 2 +- program/paper_v-full-1137.html | 2 +- program/paper_v-full-1140.html | 2 +- program/paper_v-full-1142.html | 2 +- program/paper_v-full-1147.html | 2 +- program/paper_v-full-1150.html | 2 +- program/paper_v-full-1153.html | 2 +- program/paper_v-full-1155.html | 2 +- program/paper_v-full-1179.html | 2 +- program/paper_v-full-1185.html | 2 +- program/paper_v-full-1193.html | 2 +- program/paper_v-full-1202.html | 2 +- program/paper_v-full-1204.html | 2 +- program/paper_v-full-1214.html | 2 +- program/paper_v-full-1218.html | 2 +- program/paper_v-full-1232.html | 2 +- program/paper_v-full-1251.html | 2 +- program/paper_v-full-1256.html | 2 +- program/paper_v-full-1258.html | 2 +- program/paper_v-full-1272.html | 2 +- program/paper_v-full-1275.html | 2 +- program/paper_v-full-1277.html | 2 +- program/paper_v-full-1281.html | 2 +- program/paper_v-full-1288.html | 2 +- program/paper_v-full-1290.html | 2 +- program/paper_v-full-1291.html | 2 +- program/paper_v-full-1295.html | 2 +- program/paper_v-full-1302.html | 2 +- program/paper_v-full-1307.html | 2 +- program/paper_v-full-1309.html | 2 +- program/paper_v-full-1316.html | 2 +- program/paper_v-full-1318.html | 2 +- program/paper_v-full-1325.html | 2 +- program/paper_v-full-1326.html | 2 +- program/paper_v-full-1329.html | 2 +- program/paper_v-full-1332.html | 2 +- program/paper_v-full-1333.html | 2 +- program/paper_v-full-1342.html | 2 +- program/paper_v-full-1351.html | 2 +- program/paper_v-full-1363.html | 2 +- program/paper_v-full-1368.html | 2 +- program/paper_v-full-1391.html | 2 +- program/paper_v-full-1393.html | 2 +- program/paper_v-full-1394.html | 2 +- program/paper_v-full-1395.html | 2 +- program/paper_v-full-1414.html | 2 +- program/paper_v-full-1416.html | 2 +- program/paper_v-full-1422.html | 2 +- program/paper_v-full-1425.html | 2 +- program/paper_v-full-1427.html | 2 +- program/paper_v-full-1438.html | 2 +- program/paper_v-full-1446.html | 2 +- program/paper_v-full-1451.html | 2 +- program/paper_v-full-1461.html | 2 +- program/paper_v-full-1472.html | 2 +- program/paper_v-full-1474.html | 2 +- program/paper_v-full-1480.html | 2 +- program/papers.json | 2 +- program/room_bayshore1.html | 2 +- program/room_bayshore2.html | 2 +- program/room_bayshore5.html | 2 +- program/room_esplanadesuites.html | 2 +- program/serve_paper_list.json | 2 +- program/serve_session_list.json | 2 +- program/session_associated5.html | 2 +- program/session_contest2.html | 2 +- program/session_contest3.html | 2 +- program/session_full26.html | 2 +- program/session_workshop3a.html | 2 +- 91 files changed, 470 insertions(+), 89 deletions(-) create mode 100644 program/paper_a-scivis-contest-1.html create mode 100644 program/paper_a-scivis-contest-2.html create mode 100644 program/paper_a-scivis-contest-3.html diff --git a/program/calendar.html b/program/calendar.html index 270ae1a74..e28d18aea 100644 --- a/program/calendar.html +++ b/program/calendar.html @@ -1,4 +1,4 @@ - IEEE VIS 2024 Content: Schedule
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2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

full

VIS Full Papers

Look, Learn, Language Models

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Nicole Sultanum

6 presentations in this session. See more »

full

VIS Full Papers

Where the Networks Are

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Oliver Deussen

6 presentations in this session. See more »

full

VIS Full Papers

Human and Machine Visualization Literacy

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Bum Chul Kwon

6 presentations in this session. See more »

full

VIS Full Papers

Flow, Topology, and Uncertainty

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Bei Wang

6 presentations in this session. See more »

2024-10-18T14:15:00Z – 2024-10-18T15:00:00Z

vis

Conference Events

Test of Time Awards

2024-10-18T14:15:00Z – 2024-10-18T15:00:00Z

Chair: Ross Maciejewski

1 presentations in this session. See more »

2024-10-18T15:00:00Z – 2024-10-18T16:30:00Z

vis

Conference Events

IEEE VIS Capstone and Closing

2024-10-18T15:00:00Z – 2024-10-18T16:30:00Z

Chair: Paul Rosen, Kristi Potter, Remco Chang

3 presentations in this session. See more »

2024-10-15T12:30:00Z – 2024-10-15T13:45:00Z

vis

Conference Events

Opening Session

2024-10-15T12:30:00Z – 2024-10-15T13:45:00Z

Chair: Paul Rosen, Kristi Potter, Remco Chang

2 presentations in this session. See more »

2024-10-15T14:15:00Z – 2024-10-15T15:45:00Z

short

VIS Short Papers

VGTC Awards & Best Short Papers

2024-10-15T14:15:00Z – 2024-10-15T15:45:00Z

Chair: Chaoli Wang

4 presentations in this session. See more »

2024-10-15T15:35:00Z – 2024-10-15T16:00:00Z

vis

Conference Events

VIS Governance

2024-10-15T15:35:00Z – 2024-10-15T16:00:00Z

Chair: Petra Isenberg, Jean-Daniel Fekete

2 presentations in this session. See more »

2024-10-15T16:00:00Z – 2024-10-15T17:30:00Z

full

VIS Full Papers

Best Full Papers

2024-10-15T16:00:00Z – 2024-10-15T17:30:00Z

Chair: Claudio Silva

6 presentations in this session. See more »

2024-10-15T18:00:00Z – 2024-10-15T19:00:00Z

visap

VIS Arts Program

VISAP Keynote: The Golden Age of Visualization Dissensus

2024-10-15T18:00:00Z – 2024-10-15T19:00:00Z

Chair: Pedro Cruz, Rewa Wright, Rebecca Ruige Xu, Lori Jacques, Santiago Echeverry, Kate Terrado, Todd Linkner, Alberto Cairo

0 presentations in this session. See more »

2024-10-15T19:00:00Z – 2024-10-15T21:00:00Z

vis

Conference Events

Posters

2024-10-15T19:00:00Z – 2024-10-15T21:00:00Z

0 presentations in this session. See more »

visap

VIS Arts Program

VISAP Artist Talks

2024-10-15T19:00:00Z – 2024-10-15T21:00:00Z

Chair: Pedro Cruz, Rewa Wright, Rebecca Ruige Xu, Lori Jacques, Santiago Echeverry, Kate Terrado, Todd Linkner

16 presentations in this session. See more »

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

full

VIS Full Papers

Visualization Recommendation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Johannes Knittel

6 presentations in this session. See more »

full

VIS Full Papers

Model-checking and Validation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Michael Correll

6 presentations in this session. See more »

full

VIS Full Papers

Embeddings and Document Spatialization

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Alex Endert

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Perception and Representation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Anjana Arunkumar

8 presentations in this session. See more »

panel

VIS Panels

Panel: Human-Centered Computing Research in South America: Status Quo, Opportunities, and Challenges

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Chaoli Wang

0 presentations in this session. See more »

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

full

VIS Full Papers

Applications: Sports. Games, and Finance

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Marc Streit

6 presentations in this session. See more »

full

VIS Full Papers

Visual Design: Sketching and Labeling

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Jonathan C. Roberts

6 presentations in this session. See more »

full

VIS Full Papers

Topological Data Analysis

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Ingrid Hotz

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Text and Multimedia

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Min Lu

8 presentations in this session. See more »

panel

VIS Panels

Panel: (Yet Another) Evaluation Needed? A Panel Discussion on Evaluation Trends in Visualization

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Ghulam Jilani Quadri, Danielle Albers Szafir, Arran Zeyu Wang, Hyeon Jeon

0 presentations in this session. See more »

visap

VIS Arts Program

VISAP Pictorials

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Pedro Cruz, Rewa Wright, Rebecca Ruige Xu, Lori Jacques, Santiago Echeverry, Kate Terrado, Todd Linkner

8 presentations in this session. See more »

2024-10-17T15:30:00Z – 2024-10-17T16:00:00Z

vis

Conference Events

IEEE VIS 2025 Kickoff

2024-10-17T15:30:00Z – 2024-10-17T16:00:00Z

Chair: Johanna Schmidt, Kresimir Matković, Barbora Kozlíková, Eduard Gröller

1 presentations in this session. See more »

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

full

VIS Full Papers

Once Upon a Visualization

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Marti Hearst

6 presentations in this session. See more »

full

VIS Full Papers

Visualization Design Methods

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Miriah Meyer

6 presentations in this session. See more »

full

VIS Full Papers

The Toolboxes of Visualization

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Dominik Moritz

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Analytics and Applications

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Anna Vilanova

8 presentations in this session. See more »

invited

CG&A Invited Partnership Presentations

CG&A: Systems, Theory, and Evaluations

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Francesca Samsel

6 presentations in this session. See more »

panel

VIS Panels

Panel: Vogue or Visionary? Current Challenges and Future Opportunities in Situated Visualizations

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Michelle A. Borkin, Melanie Tory

0 presentations in this session. See more »

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

full

VIS Full Papers

Journalism and Public Policy

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Sungahn Ko

6 presentations in this session. See more »

full

VIS Full Papers

Applications: Industry, Computing, and Medicine

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Joern Kohlhammer

6 presentations in this session. See more »

full

VIS Full Papers

Accessibility and Touch

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Narges Mahyar

6 presentations in this session. See more »

full

VIS Full Papers

Motion and Animated Notions

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Catherine d'Ignazio

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: AI and LLM

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Cindy Xiong Bearfield

8 presentations in this session. See more »

panel

VIS Panels

Panel: Dear Younger Me: A Dialog About Professional Development Beyond The Initial Career Phases

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Robert M Kirby, Michael Gleicher

0 presentations in this session. See more »

2024-10-16T12:30:00Z – 2024-10-16T13:30:00Z

full

VIS Full Papers

Virtual: VIS from around the world

2024-10-16T12:30:00Z – 2024-10-16T13:30:00Z

Chair: Mahmood Jasim

6 presentations in this session. See more »

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

full

VIS Full Papers

Text, Annotation, and Metaphor

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Melanie Tory

6 presentations in this session. See more »

full

VIS Full Papers

Immersive Visualization and Visual Analytics

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Lingyun Yu

6 presentations in this session. See more »

full

VIS Full Papers

Machine Learning for Visualization

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Joshua Levine

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Graph, Hierarchy and Multidimensional

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Alfie Abdul-Rahman

8 presentations in this session. See more »

panel

VIS Panels

Panel: What Do Visualization Art Projects Bring to the VIS Community?

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Xinhuan Shu, Yifang Wang, Junxiu Tang

0 presentations in this session. See more »

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

full

VIS Full Papers

Biological Data Visualization

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Nils Gehlenborg

6 presentations in this session. See more »

full

VIS Full Papers

Judgment and Decision-making

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Wenwen Dou

6 presentations in this session. See more »

full

VIS Full Papers

Time and Sequences

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Silvia Miksch

6 presentations in this session. See more »

full

VIS Full Papers

Dimensionality Reduction

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Jian Zhao

6 presentations in this session. See more »

full

VIS Full Papers

Urban Planning, Construction, and Disaster Management

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Siming Chen

6 presentations in this session. See more »

visap

VIS Arts Program

VISAP Papers

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Pedro Cruz, Rewa Wright, Rebecca Ruige Xu, Lori Jacques, Santiago Echeverry, Kate Terrado, Todd Linkner

6 presentations in this session. See more »

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

full

VIS Full Papers

Natural Language and Multimodal Interaction

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Ana Crisan

6 presentations in this session. See more »

full

VIS Full Papers

Collaboration and Communication

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Vidya Setlur

6 presentations in this session. See more »

full

VIS Full Papers

Perception and Cognition

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Bernhard Preim

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Scientific and Immersive Visualization

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Bei Wang

8 presentations in this session. See more »

invited

CG&A Invited Partnership Presentations

CG&A: Analytics and Applications

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Bruce Campbell

6 presentations in this session. See more »

panel

VIS Panels

Panel: 20 Years of Visual Analytics

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: David Ebert, Wolfgang Jentner, Ross Maciejewski, Jieqiong Zhao

0 presentations in this session. See more »

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

full

VIS Full Papers

Designing Palettes and Encodings

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Khairi Rheda

6 presentations in this session. See more »

full

VIS Full Papers

Of Nodes and Networks

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Carolina Nobre

6 presentations in this session. See more »

full

VIS Full Papers

Scripts, Notebooks, and Provenance

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Alex Lex

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: System design

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Chris Bryan

8 presentations in this session. See more »

panel

VIS Panels

Panel: Past, Present, and Future of Data Storytelling

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Haotian Li, Yun Wang, Benjamin Bach, Sheelagh Carpendale, Fanny Chevalier, Nathalie Riche

0 presentations in this session. See more »

application

Application Spotlights

Application Spotlight: Visualization within the Department of Energy

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Ana Crisan, Menna El-Assady

0 presentations in this session. See more »

2024-10-16T19:00:00Z – 2024-10-16T19:30:00Z

vis

Conference Events

IEEE VIS Town Hall

2024-10-16T19:00:00Z – 2024-10-16T19:30:00Z

Chair: Ross Maciejewski

0 presentations in this session. See more »

2024-10-16T19:30:00Z – 2024-10-16T20:30:00Z

panel

VIS Panels

Panel: VIS Conference Futures: Community Opinions on Recent Experiences, Challenges, and Opportunities for Hybrid Event Formats

2024-10-16T19:30:00Z – 2024-10-16T20:30:00Z

Chair: Matthew Brehmer, Narges Mahyar

0 presentations in this session. See more »

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

associated

VAST Challenge

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: R. Jordan Crouser, Steve Gomez, Jereme Haack

13 presentations in this session. See more »

workshop

VISxAI: 7th Workshop on Visualization for AI Explainability

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Alex Bäuerle, Angie Boggust, Fred Hohman

12 presentations in this session. See more »

workshop

1st Workshop on Accessible Data Visualization

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Brianna Wimer, Laura South

7 presentations in this session. See more »

workshop

First-Person Visualizations for Outdoor Physical Activities: Challenges and Opportunities

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Charles Perin, Tica Lin, Lijie Yao, Yalong Yang, Maxime Cordeil, Wesley Willett

0 presentations in this session. See more »

workshop

EduVis: Workshop on Visualization Education, Literacy, and Activities

EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach

3 presentations in this session. See more »

tutorial

Visualization Analysis and Design

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Tamara Munzner

0 presentations in this session. See more »

tutorial

Developing Immersive and Collaborative Visualizations with Web-Technologies

Developing Immersive and Collaborative Visualizations with Web Technologies

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: David Saffo

0 presentations in this session. See more »

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

associated

VDS: Visualization in Data Science Symposium

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Ana Crisan, Dylan Cashman

6 presentations in this session. See more »

associated

LDAV: 13th IEEE Symposium on Large Data Analysis and Visualization

LDAV: 14th IEEE Symposium on Large Data Analysis and Visualization

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Silvio Rizzi, Gunther Weber, Guido Reina, Ken Moreland

6 presentations in this session. See more »

associated

Bio+MedVis Challenges

Bio+Med+Vis Workshop

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Barbora Kozlikova, Nils Gehlenborg, Laura Garrison, Eric Mörth, Morgan Turner, Simon Warchol

6 presentations in this session. See more »

workshop

Workshop on Data Storytelling in an Era of Generative AI

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Xingyu Lan, Leni Yang, Zezhong Wang, Yun Wang, Danqing Shi, Sheelagh Carpendale

4 presentations in this session. See more »

workshop

EduVis: Workshop on Visualization Education, Literacy, and Activities

EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 2)

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Jillian Aurisano, Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

3 presentations in this session. See more »

tutorial

Generating Color Schemes for your Data Visualizations

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Theresa-Marie Rhyne

0 presentations in this session. See more »

tutorial

Running Online User Studies with the reVISit Framework

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Jack Wilburn

0 presentations in this session. See more »

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

associated

VisInPractice

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Arjun Srinivasan, Ayan Biswas

0 presentations in this session. See more »

associated

SciVis Contest

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Karen Bemis, Tim Gerrits

0 presentations in this session. See more »

workshop

BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization

BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization (Session 1)

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Anastasia Bezerianos, Michael Correll, Kyle Hall, Jürgen Bernard, Dan Keefe, Mai Elshehaly, Mahsan Nourani

6 presentations in this session. See more »

workshop

Progressive Data Analysis and Visualization (PDAV) Workshop.

Progressive Data Analysis and Visualization (PDAV) Workshop

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Alex Ulmer, Jaemin Jo, Michael Sedlmair, Jean-Daniel Fekete

3 presentations in this session. See more »

workshop

Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Tushar M. Athawale, Chris R. Johnson, Kristi Potter, Paul Rosen, David Pugmire

12 presentations in this session. See more »

workshop

Visualization for Climate Action and Sustainability

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Benjamin Bach, Fanny Chevalier, Helen-Nicole Kostis, Mark SubbaRao, Yvonne Jansen, Robert Soden

13 presentations in this session. See more »

tutorial

LLM4Vis: Large Language Models for Information Visualization

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Enamul Hoque

0 presentations in this session. See more »

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

workshop

TopoInVis: Workshop on Topological Data Analysis and Visualization

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Federico Iuricich, Yue Zhang

6 presentations in this session. See more »

workshop

BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization

BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization (Sesssion 2)

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Anastasia Bezerianos, Michael Correll, Kyle Hall, Jürgen Bernard, Dan Keefe, Mai Elshehaly, Mahsan Nourani

11 presentations in this session. See more »

workshop

NLVIZ Workshop: Exploring Research Opportunities for Natural Language, Text, and Data Visualization

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Vidya Setlur, Arjun Srinivasan

11 presentations in this session. See more »

workshop

EnergyVis 2024: 4th Workshop on Energy Data Visualization

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Kenny Gruchalla, Anjana Arunkumar, Sarah Goodwin, Arnaud Prouzeau, Lyn Bartram

11 presentations in this session. See more »

workshop

VISions of the Future: Workshop on Sustainable Practices within Visualization and Physicalisation

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Georgia Panagiotidou, Luiz Morais, Sarah Hayes, Derya Akbaba, Tatiana Losev, Andrew McNutt

5 presentations in this session. See more »

tutorial

Enabling Scientific Discovery: A Tutorial for Harnessing the Power of the National Science Data Fabric for Large-Scale Data Analysis

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Amy Gooch

0 presentations in this session. See more »

tutorial

Preparing, Conducting, and Analyzing Participatory Design Sessions for Information Visualizations

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Adriana Arcia

0 presentations in this session. See more »

IEEE VIS 2024 Content: Schedule
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2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

full

VIS Full Papers

Look, Learn, Language Models

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Nicole Sultanum

6 presentations in this session. See more »

full

VIS Full Papers

Where the Networks Are

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Oliver Deussen

6 presentations in this session. See more »

full

VIS Full Papers

Human and Machine Visualization Literacy

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Bum Chul Kwon

6 presentations in this session. See more »

full

VIS Full Papers

Flow, Topology, and Uncertainty

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Bei Wang

6 presentations in this session. See more »

2024-10-18T14:15:00Z – 2024-10-18T15:00:00Z

vis

Conference Events

Test of Time Awards

2024-10-18T14:15:00Z – 2024-10-18T15:00:00Z

Chair: Ross Maciejewski

1 presentations in this session. See more »

2024-10-18T15:00:00Z – 2024-10-18T16:30:00Z

vis

Conference Events

IEEE VIS Capstone and Closing

2024-10-18T15:00:00Z – 2024-10-18T16:30:00Z

Chair: Paul Rosen, Kristi Potter, Remco Chang

3 presentations in this session. See more »

2024-10-15T12:30:00Z – 2024-10-15T13:45:00Z

vis

Conference Events

Opening Session

2024-10-15T12:30:00Z – 2024-10-15T13:45:00Z

Chair: Paul Rosen, Kristi Potter, Remco Chang

2 presentations in this session. See more »

2024-10-15T14:15:00Z – 2024-10-15T15:45:00Z

short

VIS Short Papers

VGTC Awards & Best Short Papers

2024-10-15T14:15:00Z – 2024-10-15T15:45:00Z

Chair: Chaoli Wang

4 presentations in this session. See more »

2024-10-15T15:35:00Z – 2024-10-15T16:00:00Z

vis

Conference Events

VIS Governance

2024-10-15T15:35:00Z – 2024-10-15T16:00:00Z

Chair: Petra Isenberg, Jean-Daniel Fekete

2 presentations in this session. See more »

2024-10-15T16:00:00Z – 2024-10-15T17:30:00Z

full

VIS Full Papers

Best Full Papers

2024-10-15T16:00:00Z – 2024-10-15T17:30:00Z

Chair: Claudio Silva

6 presentations in this session. See more »

2024-10-15T18:00:00Z – 2024-10-15T19:00:00Z

visap

VIS Arts Program

VISAP Keynote: The Golden Age of Visualization Dissensus

2024-10-15T18:00:00Z – 2024-10-15T19:00:00Z

Chair: Pedro Cruz, Rewa Wright, Rebecca Ruige Xu, Lori Jacques, Santiago Echeverry, Kate Terrado, Todd Linkner, Alberto Cairo

0 presentations in this session. See more »

2024-10-15T19:00:00Z – 2024-10-15T21:00:00Z

vis

Conference Events

Posters

2024-10-15T19:00:00Z – 2024-10-15T21:00:00Z

0 presentations in this session. See more »

visap

VIS Arts Program

VISAP Artist Talks

2024-10-15T19:00:00Z – 2024-10-15T21:00:00Z

Chair: Pedro Cruz, Rewa Wright, Rebecca Ruige Xu, Lori Jacques, Santiago Echeverry, Kate Terrado, Todd Linkner

16 presentations in this session. See more »

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

full

VIS Full Papers

Visualization Recommendation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Johannes Knittel

6 presentations in this session. See more »

full

VIS Full Papers

Model-checking and Validation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Michael Correll

6 presentations in this session. See more »

full

VIS Full Papers

Embeddings and Document Spatialization

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Alex Endert

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Perception and Representation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Anjana Arunkumar

8 presentations in this session. See more »

panel

VIS Panels

Panel: Human-Centered Computing Research in South America: Status Quo, Opportunities, and Challenges

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Chaoli Wang

0 presentations in this session. See more »

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

full

VIS Full Papers

Applications: Sports. Games, and Finance

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Marc Streit

6 presentations in this session. See more »

full

VIS Full Papers

Visual Design: Sketching and Labeling

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Jonathan C. Roberts

6 presentations in this session. See more »

full

VIS Full Papers

Topological Data Analysis

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Ingrid Hotz

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Text and Multimedia

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Min Lu

8 presentations in this session. See more »

panel

VIS Panels

Panel: (Yet Another) Evaluation Needed? A Panel Discussion on Evaluation Trends in Visualization

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Ghulam Jilani Quadri, Danielle Albers Szafir, Arran Zeyu Wang, Hyeon Jeon

0 presentations in this session. See more »

visap

VIS Arts Program

VISAP Pictorials

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Pedro Cruz, Rewa Wright, Rebecca Ruige Xu, Lori Jacques, Santiago Echeverry, Kate Terrado, Todd Linkner

8 presentations in this session. See more »

2024-10-17T15:30:00Z – 2024-10-17T16:00:00Z

vis

Conference Events

IEEE VIS 2025 Kickoff

2024-10-17T15:30:00Z – 2024-10-17T16:00:00Z

Chair: Johanna Schmidt, Kresimir Matković, Barbora Kozlíková, Eduard Gröller

1 presentations in this session. See more »

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

full

VIS Full Papers

Once Upon a Visualization

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Marti Hearst

6 presentations in this session. See more »

full

VIS Full Papers

Visualization Design Methods

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Miriah Meyer

6 presentations in this session. See more »

full

VIS Full Papers

The Toolboxes of Visualization

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Dominik Moritz

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Analytics and Applications

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Anna Vilanova

8 presentations in this session. See more »

invited

CG&A Invited Partnership Presentations

CG&A: Systems, Theory, and Evaluations

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Francesca Samsel

6 presentations in this session. See more »

panel

VIS Panels

Panel: Vogue or Visionary? Current Challenges and Future Opportunities in Situated Visualizations

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Michelle A. Borkin, Melanie Tory

0 presentations in this session. See more »

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

full

VIS Full Papers

Journalism and Public Policy

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Sungahn Ko

6 presentations in this session. See more »

full

VIS Full Papers

Applications: Industry, Computing, and Medicine

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Joern Kohlhammer

6 presentations in this session. See more »

full

VIS Full Papers

Accessibility and Touch

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Narges Mahyar

6 presentations in this session. See more »

full

VIS Full Papers

Motion and Animated Notions

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Catherine d'Ignazio

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: AI and LLM

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Cindy Xiong Bearfield

8 presentations in this session. See more »

panel

VIS Panels

Panel: Dear Younger Me: A Dialog About Professional Development Beyond The Initial Career Phases

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Robert M Kirby, Michael Gleicher

0 presentations in this session. See more »

2024-10-16T12:30:00Z – 2024-10-16T13:30:00Z

full

VIS Full Papers

Virtual: VIS from around the world

2024-10-16T12:30:00Z – 2024-10-16T13:30:00Z

Chair: Mahmood Jasim

6 presentations in this session. See more »

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

full

VIS Full Papers

Text, Annotation, and Metaphor

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Melanie Tory

6 presentations in this session. See more »

full

VIS Full Papers

Immersive Visualization and Visual Analytics

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Lingyun Yu

6 presentations in this session. See more »

full

VIS Full Papers

Machine Learning for Visualization

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Joshua Levine

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Graph, Hierarchy and Multidimensional

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Alfie Abdul-Rahman

8 presentations in this session. See more »

panel

VIS Panels

Panel: What Do Visualization Art Projects Bring to the VIS Community?

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Xinhuan Shu, Yifang Wang, Junxiu Tang

0 presentations in this session. See more »

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

full

VIS Full Papers

Biological Data Visualization

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Nils Gehlenborg

6 presentations in this session. See more »

full

VIS Full Papers

Judgment and Decision-making

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Wenwen Dou

6 presentations in this session. See more »

full

VIS Full Papers

Time and Sequences

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Silvia Miksch

6 presentations in this session. See more »

full

VIS Full Papers

Dimensionality Reduction

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Jian Zhao

6 presentations in this session. See more »

full

VIS Full Papers

Urban Planning, Construction, and Disaster Management

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Siming Chen

6 presentations in this session. See more »

visap

VIS Arts Program

VISAP Papers

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Pedro Cruz, Rewa Wright, Rebecca Ruige Xu, Lori Jacques, Santiago Echeverry, Kate Terrado, Todd Linkner

6 presentations in this session. See more »

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

full

VIS Full Papers

Natural Language and Multimodal Interaction

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Ana Crisan

6 presentations in this session. See more »

full

VIS Full Papers

Collaboration and Communication

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Vidya Setlur

6 presentations in this session. See more »

full

VIS Full Papers

Perception and Cognition

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Tamara Munzner

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: Scientific and Immersive Visualization

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Bei Wang

8 presentations in this session. See more »

invited

CG&A Invited Partnership Presentations

CG&A: Analytics and Applications

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Bruce Campbell

6 presentations in this session. See more »

panel

VIS Panels

Panel: 20 Years of Visual Analytics

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: David Ebert, Wolfgang Jentner, Ross Maciejewski, Jieqiong Zhao

0 presentations in this session. See more »

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

full

VIS Full Papers

Designing Palettes and Encodings

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Khairi Rheda

6 presentations in this session. See more »

full

VIS Full Papers

Of Nodes and Networks

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Carolina Nobre

6 presentations in this session. See more »

full

VIS Full Papers

Scripts, Notebooks, and Provenance

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Alex Lex

6 presentations in this session. See more »

short

VIS Short Papers

Short Papers: System design

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Chris Bryan

8 presentations in this session. See more »

panel

VIS Panels

Panel: Past, Present, and Future of Data Storytelling

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Haotian Li, Yun Wang, Benjamin Bach, Sheelagh Carpendale, Fanny Chevalier, Nathalie Riche

0 presentations in this session. See more »

application

Application Spotlights

Application Spotlight: Visualization within the Department of Energy

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Ana Crisan, Menna El-Assady

0 presentations in this session. See more »

2024-10-16T19:00:00Z – 2024-10-16T19:30:00Z

vis

Conference Events

IEEE VIS Town Hall

2024-10-16T19:00:00Z – 2024-10-16T19:30:00Z

Chair: Ross Maciejewski

0 presentations in this session. See more »

2024-10-16T19:30:00Z – 2024-10-16T20:30:00Z

panel

VIS Panels

Panel: VIS Conference Futures: Community Opinions on Recent Experiences, Challenges, and Opportunities for Hybrid Event Formats

2024-10-16T19:30:00Z – 2024-10-16T20:30:00Z

Chair: Matthew Brehmer, Narges Mahyar

0 presentations in this session. See more »

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

associated

VAST Challenge

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: R. Jordan Crouser, Steve Gomez, Jereme Haack

10 presentations in this session. See more »

workshop

VISxAI: 7th Workshop on Visualization for AI Explainability

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Alex Bäuerle, Angie Boggust, Fred Hohman

12 presentations in this session. See more »

workshop

1st Workshop on Accessible Data Visualization

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Brianna Wimer, Laura South

7 presentations in this session. See more »

workshop

First-Person Visualizations for Outdoor Physical Activities: Challenges and Opportunities

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Charles Perin, Tica Lin, Lijie Yao, Yalong Yang, Maxime Cordeil, Wesley Willett

0 presentations in this session. See more »

workshop

EduVis: Workshop on Visualization Education, Literacy, and Activities

EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

3 presentations in this session. See more »

tutorial

Visualization Analysis and Design

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Tamara Munzner

0 presentations in this session. See more »

tutorial

Developing Immersive and Collaborative Visualizations with Web-Technologies

Developing Immersive and Collaborative Visualizations with Web Technologies

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: David Saffo

0 presentations in this session. See more »

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

associated

VDS: Visualization in Data Science Symposium

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Ana Crisan, Dylan Cashman, Saugat Pandey, Alvitta Ottley, John E Wenskovitch

6 presentations in this session. See more »

associated

LDAV: 13th IEEE Symposium on Large Data Analysis and Visualization

LDAV: 14th IEEE Symposium on Large Data Analysis and Visualization

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Silvio Rizzi, Gunther Weber, Guido Reina, Ken Moreland

6 presentations in this session. See more »

associated

Bio+MedVis Challenges

Bio+Med+Vis Workshop

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Barbora Kozlikova, Nils Gehlenborg, Laura Garrison, Eric Mörth, Morgan Turner, Simon Warchol

6 presentations in this session. See more »

workshop

Workshop on Data Storytelling in an Era of Generative AI

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Xingyu Lan, Leni Yang, Zezhong Wang, Yun Wang, Danqing Shi, Sheelagh Carpendale

4 presentations in this session. See more »

workshop

EduVis: Workshop on Visualization Education, Literacy, and Activities

EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 2)

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Jillian Aurisano, Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

3 presentations in this session. See more »

tutorial

Generating Color Schemes for your Data Visualizations

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Theresa-Marie Rhyne

0 presentations in this session. See more »

tutorial

Running Online User Studies with the reVISit Framework

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Jack Wilburn

0 presentations in this session. See more »

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

associated

VisInPractice

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Arjun Srinivasan, Ayan Biswas

0 presentations in this session. See more »

associated

SciVis Contest

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Karen Bemis, Tim Gerrits

3 presentations in this session. See more »

workshop

BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization

BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization (Session 1)

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Anastasia Bezerianos, Michael Correll, Kyle Hall, Jürgen Bernard, Dan Keefe, Mai Elshehaly, Mahsan Nourani

6 presentations in this session. See more »

workshop

Progressive Data Analysis and Visualization (PDAV) Workshop.

Progressive Data Analysis and Visualization (PDAV) Workshop

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Alex Ulmer, Jaemin Jo, Michael Sedlmair, Jean-Daniel Fekete

3 presentations in this session. See more »

workshop

Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Tushar M. Athawale, Chris R. Johnson, Kristi Potter, Paul Rosen, David Pugmire

12 presentations in this session. See more »

workshop

Visualization for Climate Action and Sustainability

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Benjamin Bach, Fanny Chevalier, Helen-Nicole Kostis, Mark SubbaRao, Yvonne Jansen, Robert Soden

13 presentations in this session. See more »

tutorial

LLM4Vis: Large Language Models for Information Visualization

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Enamul Hoque

0 presentations in this session. See more »

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

workshop

TopoInVis: Workshop on Topological Data Analysis and Visualization

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Federico Iuricich, Yue Zhang

6 presentations in this session. See more »

workshop

BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization

BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization (Sesssion 2)

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Anastasia Bezerianos, Michael Correll, Kyle Hall, Jürgen Bernard, Dan Keefe, Mai Elshehaly, Mahsan Nourani

11 presentations in this session. See more »

workshop

NLVIZ Workshop: Exploring Research Opportunities for Natural Language, Text, and Data Visualization

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Vidya Setlur, Arjun Srinivasan

11 presentations in this session. See more »

workshop

EnergyVis 2024: 4th Workshop on Energy Data Visualization

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Kenny Gruchalla, Anjana Arunkumar, Sarah Goodwin, Arnaud Prouzeau, Lyn Bartram

11 presentations in this session. See more »

workshop

VISions of the Future: Workshop on Sustainable Practices within Visualization and Physicalisation

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Georgia Panagiotidou, Luiz Morais, Sarah Hayes, Derya Akbaba, Tatiana Losev, Andrew McNutt

5 presentations in this session. See more »

tutorial

Enabling Scientific Discovery: A Tutorial for Harnessing the Power of the National Science Data Fabric for Large-Scale Data Analysis

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Amy Gooch

0 presentations in this session. See more »

tutorial

Preparing, Conducting, and Analyzing Participatory Design Sessions for Information Visualizations

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z

Chair: Adriana Arcia

0 presentations in this session. See more »

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IEEE VIS 2024 Content: SciVis Contest

SciVis Contest

https://ieeevis.org/year/2024/program/event_a-scivis-contest.html

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

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associated

SciVis Contest

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Karen Bemis, Tim Gerrits

0 presentations in this session. See more »

IEEE VIS 2024 Content: SciVis Contest

SciVis Contest

https://ieeevis.org/year/2024/program/event_a-scivis-contest.html

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

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associated

SciVis Contest

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Chair: Karen Bemis, Tim Gerrits

3 presentations in this session. See more »

IEEE VIS 2024 Content: VAST Challenge

VAST Challenge

https://ieeevis.org/year/2024/program/event_a-vast-challenge.html

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

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associated

VAST Challenge

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: R. Jordan Crouser, Steve Gomez, Jereme Haack

13 presentations in this session. See more »

IEEE VIS 2024 Content: VAST Challenge

VAST Challenge

https://ieeevis.org/year/2024/program/event_a-vast-challenge.html

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

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associated

VAST Challenge

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: R. Jordan Crouser, Steve Gomez, Jereme Haack

10 presentations in this session. See more »

IEEE VIS 2024 Content: VDS: Visualization in Data Science Symposium

VDS: Visualization in Data Science Symposium

https://ieeevis.org/year/2024/program/event_s-vds.html

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

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associated

VDS: Visualization in Data Science Symposium

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Ana Crisan, Dylan Cashman

6 presentations in this session. See more »

IEEE VIS 2024 Content: VDS: Visualization in Data Science Symposium

VDS: Visualization in Data Science Symposium

https://ieeevis.org/year/2024/program/event_s-vds.html

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

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associated

VDS: Visualization in Data Science Symposium

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Ana Crisan, Dylan Cashman, Saugat Pandey, Alvitta Ottley, John E Wenskovitch

6 presentations in this session. See more »

IEEE VIS 2024 Content: VIS Full Papers

VIS Full Papers

https://ieeevis.org/year/2024/program/event_v-full.html

2024-10-15T16:00:00Z – 2024-10-16T13:30:00Z

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full

Best Full Papers

2024-10-15T16:00:00Z – 2024-10-15T17:30:00Z

Chair: Claudio Silva

6 presentations in this session. See more »

full

Applications: Sports. Games, and Finance

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Marc Streit

6 presentations in this session. See more »

full

Designing Palettes and Encodings

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Khairi Rheda

6 presentations in this session. See more »

full

Text, Annotation, and Metaphor

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Melanie Tory

6 presentations in this session. See more »

full

Journalism and Public Policy

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Sungahn Ko

6 presentations in this session. See more »

full

Natural Language and Multimodal Interaction

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Ana Crisan

6 presentations in this session. See more »

full

Look, Learn, Language Models

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Nicole Sultanum

6 presentations in this session. See more »

full

Biological Data Visualization

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Nils Gehlenborg

6 presentations in this session. See more »

full

Immersive Visualization and Visual Analytics

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Lingyun Yu

6 presentations in this session. See more »

full

Machine Learning for Visualization

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Joshua Levine

6 presentations in this session. See more »

full

Where the Networks Are

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Oliver Deussen

6 presentations in this session. See more »

full

Visualization Recommendation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Johannes Knittel

6 presentations in this session. See more »

full

Applications: Industry, Computing, and Medicine

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Joern Kohlhammer

6 presentations in this session. See more »

full

Judgment and Decision-making

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Wenwen Dou

6 presentations in this session. See more »

full

Model-checking and Validation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Michael Correll

6 presentations in this session. See more »

full

Time and Sequences

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Silvia Miksch

6 presentations in this session. See more »

full

Accessibility and Touch

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Narges Mahyar

6 presentations in this session. See more »

full

Collaboration and Communication

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Vidya Setlur

6 presentations in this session. See more »

full

Once Upon a Visualization

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Marti Hearst

6 presentations in this session. See more »

full

Perception and Cognition

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Bernhard Preim

6 presentations in this session. See more »

full

Of Nodes and Networks

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Carolina Nobre

6 presentations in this session. See more »

full

Human and Machine Visualization Literacy

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Bum Chul Kwon

6 presentations in this session. See more »

full

Visualization Design Methods

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Miriah Meyer

6 presentations in this session. See more »

full

Flow, Topology, and Uncertainty

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Bei Wang

6 presentations in this session. See more »

full

Scripts, Notebooks, and Provenance

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Alex Lex

6 presentations in this session. See more »

full

Visual Design: Sketching and Labeling

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Jonathan C. Roberts

6 presentations in this session. See more »

full

The Toolboxes of Visualization

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Dominik Moritz

6 presentations in this session. See more »

full

Topological Data Analysis

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Ingrid Hotz

6 presentations in this session. See more »

full

Motion and Animated Notions

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Catherine d'Ignazio

6 presentations in this session. See more »

full

Dimensionality Reduction

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Jian Zhao

6 presentations in this session. See more »

full

Urban Planning, Construction, and Disaster Management

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Siming Chen

6 presentations in this session. See more »

full

Embeddings and Document Spatialization

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Alex Endert

6 presentations in this session. See more »

full

Virtual: VIS from around the world

2024-10-16T12:30:00Z – 2024-10-16T13:30:00Z

Chair: Mahmood Jasim

6 presentations in this session. See more »

IEEE VIS 2024 Content: VIS Full Papers

VIS Full Papers

https://ieeevis.org/year/2024/program/event_v-full.html

2024-10-15T16:00:00Z – 2024-10-16T13:30:00Z

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full

Best Full Papers

2024-10-15T16:00:00Z – 2024-10-15T17:30:00Z

Chair: Claudio Silva

6 presentations in this session. See more »

full

Applications: Sports. Games, and Finance

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Marc Streit

6 presentations in this session. See more »

full

Designing Palettes and Encodings

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Khairi Rheda

6 presentations in this session. See more »

full

Text, Annotation, and Metaphor

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Melanie Tory

6 presentations in this session. See more »

full

Journalism and Public Policy

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Sungahn Ko

6 presentations in this session. See more »

full

Natural Language and Multimodal Interaction

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Ana Crisan

6 presentations in this session. See more »

full

Look, Learn, Language Models

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Nicole Sultanum

6 presentations in this session. See more »

full

Biological Data Visualization

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Nils Gehlenborg

6 presentations in this session. See more »

full

Immersive Visualization and Visual Analytics

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Lingyun Yu

6 presentations in this session. See more »

full

Machine Learning for Visualization

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z

Chair: Joshua Levine

6 presentations in this session. See more »

full

Where the Networks Are

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Oliver Deussen

6 presentations in this session. See more »

full

Visualization Recommendation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Johannes Knittel

6 presentations in this session. See more »

full

Applications: Industry, Computing, and Medicine

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Joern Kohlhammer

6 presentations in this session. See more »

full

Judgment and Decision-making

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Wenwen Dou

6 presentations in this session. See more »

full

Model-checking and Validation

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Michael Correll

6 presentations in this session. See more »

full

Time and Sequences

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Silvia Miksch

6 presentations in this session. See more »

full

Accessibility and Touch

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Narges Mahyar

6 presentations in this session. See more »

full

Collaboration and Communication

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Vidya Setlur

6 presentations in this session. See more »

full

Once Upon a Visualization

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Marti Hearst

6 presentations in this session. See more »

full

Perception and Cognition

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Chair: Tamara Munzner

6 presentations in this session. See more »

full

Of Nodes and Networks

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Carolina Nobre

6 presentations in this session. See more »

full

Human and Machine Visualization Literacy

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Bum Chul Kwon

6 presentations in this session. See more »

full

Visualization Design Methods

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Miriah Meyer

6 presentations in this session. See more »

full

Flow, Topology, and Uncertainty

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z

Chair: Bei Wang

6 presentations in this session. See more »

full

Scripts, Notebooks, and Provenance

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z

Chair: Alex Lex

6 presentations in this session. See more »

full

Visual Design: Sketching and Labeling

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Jonathan C. Roberts

6 presentations in this session. See more »

full

The Toolboxes of Visualization

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z

Chair: Dominik Moritz

6 presentations in this session. See more »

full

Topological Data Analysis

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z

Chair: Ingrid Hotz

6 presentations in this session. See more »

full

Motion and Animated Notions

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z

Chair: Catherine d'Ignazio

6 presentations in this session. See more »

full

Dimensionality Reduction

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Jian Zhao

6 presentations in this session. See more »

full

Urban Planning, Construction, and Disaster Management

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z

Chair: Siming Chen

6 presentations in this session. See more »

full

Embeddings and Document Spatialization

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z

Chair: Alex Endert

6 presentations in this session. See more »

full

Virtual: VIS from around the world

2024-10-16T12:30:00Z – 2024-10-16T13:30:00Z

Chair: Mahmood Jasim

6 presentations in this session. See more »

IEEE VIS 2024 Content: EduVis: Workshop on Visualization Education, Literacy, and Activities

EduVis: Workshop on Visualization Education, Literacy, and Activities

https://ieeevis.org/year/2024/program/event_w-eduvis.html

2024-10-13T12:30:00Z – 2024-10-13T19:00:00Z

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workshop

EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach

3 presentations in this session. See more »

workshop

EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 2)

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Jillian Aurisano, Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

3 presentations in this session. See more »

IEEE VIS 2024 Content: EduVis: Workshop on Visualization Education, Literacy, and Activities

EduVis: Workshop on Visualization Education, Literacy, and Activities

https://ieeevis.org/year/2024/program/event_w-eduvis.html

2024-10-13T12:30:00Z – 2024-10-13T19:00:00Z

Add all of this event's sessions to your calendar.
workshop

EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Chair: Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

3 presentations in this session. See more »

workshop

EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 2)

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Chair: Jillian Aurisano, Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

3 presentations in this session. See more »

IEEE VIS 2024 Content: PlumeViz: Interactive Exploration for Multi-Facet Features of Hydrothermal Plumes in Sonar Images

PlumeViz: Interactive Exploration for Multi-Facet Features of Hydrothermal Plumes in Sonar Images

Yiming Shao -

Chengming Liu -

Zhiyuan Meng -

Shufan Qian -

Peng Jiang -

Yunhai Wang -

Dr. Qiong Zeng -

Room: Bayshore V

2024-10-14T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-14T12:30:00Z
Abstract

\ No newline at end of file diff --git a/program/paper_a-scivis-contest-2.html b/program/paper_a-scivis-contest-2.html new file mode 100644 index 000000000..d47810f2d --- /dev/null +++ b/program/paper_a-scivis-contest-2.html @@ -0,0 +1,127 @@ + IEEE VIS 2024 Content: Visualization of Sonar Imaging for Hydrothermal Systems

Visualization of Sonar Imaging for Hydrothermal Systems

Ngan V. T. Nguyen -

Minh N. A. Tran -

Si Chi Hoang -

Vuong Tran Thien -

Nguyen Tran Nguyen Thanh -

Ngo Ly -

Phuc Thien Nguyen -

Sinh Huy Gip -

Sang Thanh Ngo -

Nguyễn Thái Hòa -

Room: Bayshore V

2024-10-14T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-14T12:30:00Z
Abstract

\ No newline at end of file diff --git a/program/paper_a-scivis-contest-3.html b/program/paper_a-scivis-contest-3.html new file mode 100644 index 000000000..bf146876c --- /dev/null +++ b/program/paper_a-scivis-contest-3.html @@ -0,0 +1,127 @@ + IEEE VIS 2024 Content: Topology Based Visualization of Hydrothermal Plumes

Topology Based Visualization of Hydrothermal Plumes

Adhitya Kamakshidasan -

Harikrishnan Pattathil -

Room: Bayshore V

2024-10-14T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-14T12:30:00Z
Abstract

\ No newline at end of file diff --git a/program/paper_v-full-1026.html b/program/paper_v-full-1026.html index 6b100fa4c..69c71589c 100644 --- a/program/paper_v-full-1026.html +++ b/program/paper_v-full-1026.html @@ -1,4 +1,4 @@ - IEEE VIS 2024 Content: Revealing Interaction Dynamics: Multi-Level Visual Exploration of User Strategies with an Interactive Digital Environment

Revealing Interaction Dynamics: Multi-Level Visual Exploration of User Strategies with an Interactive Digital Environment

Peilin Yu - Linköping University, Norrköping, Sweden

Aida Nordman - Linköping University, Norrköping, Sweden

Marta M. Koc-Januchta - Linköping University, Norrköping, Sweden

Konrad J Schönborn - Linköping University, Norrköping, Sweden. Linköping University, Norrköping, Sweden

Lonni Besançon - Linköping University, Norrköping, Sweden

Katerina Vrotsou - Linköping University, Norrköping, Sweden

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Room: Bayshore VI

2024-10-16T14:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:15:00Z
Exemplar figure, described by caption below
The main components of VISID comprise an Individual View (upper) and a Comparison View (lower). The Individual View can be alternated to visualize: (a) a participant's attribute change and interaction event sequences, or (b) their interface event sequences representing the concurrently opened infopanels and their lifetime duration. The Comparison View consists of three parts. From left to right, it visualizes interaction sequences ranked by the similarity score to a baseline participant in descending order. The similarity score bars and delta values (middle) depict the similarity/dissimilarity with respect to the baseline participant. The Cluster View (right) shows potential clusters of similar participants.
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Keywords

Visual analytics, Visualization systems and tools, Interaction logs, Visualization techniques, Visual learning

Abstract

We present a visual analytics approach for multi-level visual exploration of users' interaction strategies in an interactive digital environment. The use of interactive touchscreen exhibits in informal learning environments, such as museums and science centers, often incorporate frameworks that classify learning processes, such as Bloom’s taxonomy, to achieve better user engagement and knowledge transfer. To analyze user behavior within these digital environments, interaction logs are recorded to capture diverse exploration strategies. However, analysis of such logs is challenging, especially in terms of coupling interactions and cognitive learning processes, and existing work within learning and educational contexts remains limited. To address these gaps, we develop a visual analytics approach for analyzing interaction logs that supports exploration at the individual user level and multi-user comparison. The approach utilizes algorithmic methods to identify similarities in users' interactions and reveal their exploration strategies. We motivate and illustrate our approach through an application scenario, using event sequences derived from interaction log data in an experimental study conducted with science center visitors from diverse backgrounds and demographics. The study involves 14 users completing tasks of increasing complexity, designed to stimulate different levels of cognitive learning processes. We implement our approach in an interactive visual analytics prototype system, named VISID, and together with domain experts, discover a set of task-solving exploration strategies, such as "cascading" and "nested-loop", which reflect different levels of learning processes from Bloom's taxonomy. Finally, we discuss the generalizability and scalability of the presented system and the need for further research with data acquired in the wild.

IEEE VIS 2024 Content: Revealing Interaction Dynamics: Multi-Level Visual Exploration of User Strategies with an Interactive Digital Environment

Revealing Interaction Dynamics: Multi-Level Visual Exploration of User Strategies with an Interactive Digital Environment

Peilin Yu - Linköping University, Norrköping, Sweden

Aida Nordman - Linköping University, Norrköping, Sweden

Marta M. Koc-Januchta - Linköping University, Norrköping, Sweden

Konrad J Schönborn - Linköping University, Norrköping, Sweden. Linköping University, Norrköping, Sweden

Lonni Besançon - Linköping University, Norrköping, Sweden

Katerina Vrotsou - Linköping University, Norrköping, Sweden

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Room: Bayshore VI

2024-10-16T14:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:15:00Z
Exemplar figure, described by caption below
The main components of VISID comprise an Individual View (upper) and a Comparison View (lower). The Individual View can be alternated to visualize: (a) a participant's attribute change and interaction event sequences, or (b) their interface event sequences representing the concurrently opened infopanels and their lifetime duration. The Comparison View consists of three parts. From left to right, it visualizes interaction sequences ranked by the similarity score to a baseline participant in descending order. The similarity score bars and delta values (middle) depict the similarity/dissimilarity with respect to the baseline participant. The Cluster View (right) shows potential clusters of similar participants.
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Keywords

Visual analytics, Visualization systems and tools, Interaction logs, Visualization techniques, Visual learning

Abstract

We present a visual analytics approach for multi-level visual exploration of users' interaction strategies in an interactive digital environment. The use of interactive touchscreen exhibits in informal learning environments, such as museums and science centers, often incorporate frameworks that classify learning processes, such as Bloom’s taxonomy, to achieve better user engagement and knowledge transfer. To analyze user behavior within these digital environments, interaction logs are recorded to capture diverse exploration strategies. However, analysis of such logs is challenging, especially in terms of coupling interactions and cognitive learning processes, and existing work within learning and educational contexts remains limited. To address these gaps, we develop a visual analytics approach for analyzing interaction logs that supports exploration at the individual user level and multi-user comparison. The approach utilizes algorithmic methods to identify similarities in users' interactions and reveal their exploration strategies. We motivate and illustrate our approach through an application scenario, using event sequences derived from interaction log data in an experimental study conducted with science center visitors from diverse backgrounds and demographics. The study involves 14 users completing tasks of increasing complexity, designed to stimulate different levels of cognitive learning processes. We implement our approach in an interactive visual analytics prototype system, named VISID, and together with domain experts, discover a set of task-solving exploration strategies, such as "cascading" and "nested-loop", which reflect different levels of learning processes from Bloom's taxonomy. Finally, we discuss the generalizability and scalability of the presented system and the need for further research with data acquired in the wild.

IEEE VIS 2024 Content: Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting

Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting

Anqi Cao - Zhejiang University, Hangzhou, China

Xiao Xie - Zhejiang University, Hangzhou, China

Runjin Zhang - Zhejiang University, Hangzhou, China

Yuxin Tian - Zhejiang University, Hangzhou, China

Mu Fan - Zhejiang University, Hangzhou, China

Hui Zhang - Zhejiang University, Hangzhou, China

Yingcai Wu - Zhejiang University, Hangzhou, China

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Room: Bayshore V

2024-10-17T14:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T14:15:00Z
Exemplar figure, described by caption below
System user interface. The interface contains two views: a navigation view (A) and an investigation view (B). The navigation view consists of a squad board (A1) to navigate players will be replaced and a player ranking list (A2) to compare players by personal information and performances. The investigation view includes an on-ball tactic list (B1) for exploring essential on-ball tactics, a player record list (B2) to compare players' simulated actions under a certain on-ball tactic, and a simulated action map (B3) to display players' detailed simulated actions.
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Keywords

Soccer Visualization, Player Scouting, Design Study

Abstract

In soccer, player scouting aims to find players suitable for a team to increase the winning chance in future matches. To scout suitable players, coaches and analysts need to consider whether the players will perform well in a new team, which is hard to learn directly from their historical performances. Match simulation methods have been introduced to scout players by estimating their expected contributions to a new team. However, they usually focus on the simulation of match results and hardly support interactive analysis to navigate potential target players and compare them in fine-grained simulated behaviors. In this work, we propose a visual analytics method to assist soccer player scouting based on match simulation. We construct a two-level match simulation framework for estimating both match results and player behaviors when a player comes to a new team. Based on the framework, we develop a visual analytics system, Team-Scouter, to facilitate the simulative-based soccer player scouting process through player navigation, comparison, and investigation. With our system, coaches and analysts can find potential players suitable for the team and compare them on historical and expected performances. For an in-depth investigation of the players' expected performances, the system provides a visual comparison between the simulated behaviors of the player and the actual ones. The usefulness and effectiveness of the system are demonstrated by two case studies on a real-world dataset and an expert interview.

IEEE VIS 2024 Content: Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting

Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting

Anqi Cao - Zhejiang University, Hangzhou, China

Xiao Xie - Zhejiang University, Hangzhou, China

Runjin Zhang - Zhejiang University, Hangzhou, China

Yuxin Tian - Zhejiang University, Hangzhou, China

Mu Fan - Zhejiang University, Hangzhou, China

Hui Zhang - Zhejiang University, Hangzhou, China

Yingcai Wu - Zhejiang University, Hangzhou, China

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Room: Bayshore V

2024-10-17T14:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T14:15:00Z
Exemplar figure, described by caption below
System user interface. The interface contains two views: a navigation view (A) and an investigation view (B). The navigation view consists of a squad board (A1) to navigate players will be replaced and a player ranking list (A2) to compare players by personal information and performances. The investigation view includes an on-ball tactic list (B1) for exploring essential on-ball tactics, a player record list (B2) to compare players' simulated actions under a certain on-ball tactic, and a simulated action map (B3) to display players' detailed simulated actions.
Fast forward
Keywords

Soccer Visualization, Player Scouting, Design Study

Abstract

In soccer, player scouting aims to find players suitable for a team to increase the winning chance in future matches. To scout suitable players, coaches and analysts need to consider whether the players will perform well in a new team, which is hard to learn directly from their historical performances. Match simulation methods have been introduced to scout players by estimating their expected contributions to a new team. However, they usually focus on the simulation of match results and hardly support interactive analysis to navigate potential target players and compare them in fine-grained simulated behaviors. In this work, we propose a visual analytics method to assist soccer player scouting based on match simulation. We construct a two-level match simulation framework for estimating both match results and player behaviors when a player comes to a new team. Based on the framework, we develop a visual analytics system, Team-Scouter, to facilitate the simulative-based soccer player scouting process through player navigation, comparison, and investigation. With our system, coaches and analysts can find potential players suitable for the team and compare them on historical and expected performances. For an in-depth investigation of the players' expected performances, the system provides a visual comparison between the simulated behaviors of the player and the actual ones. The usefulness and effectiveness of the system are demonstrated by two case studies on a real-world dataset and an expert interview.

IEEE VIS 2024 Content: Visualizing Temporal Topic Embeddings with a Compass

Visualizing Temporal Topic Embeddings with a Compass

Daniel Palamarchuk - Virginia Tech, Blacksburg, United States

Lemara Williams - Virginia Polytechnic Institute of Technology , Blacksburg, United States

Brian Mayer - Virginia Tech, Blacksburg, United States

Thomas Danielson - Savannah River National Laboratory, Aiken, United States

Rebecca Faust - Tulane University, New Orleans, United States

Larry M Deschaine PhD - Savannah River National Laboratory, Aiken, United States

Chris North - Virginia Tech, Blacksburg, United States

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Room: Bayshore I

2024-10-17T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T12:30:00Z
Exemplar figure, described by caption below
We present the dynamic topic modeling method called Temporal Topic Embeddings with a Compass. The top-right image illustrates how this method effectively generates a plot of term movements within the context of documents and their associated topics. The outer image showcases TimeLink, a tool that compares word vectors in both global and local topic contexts. The red boxes correspond to the respective time periods: the time represented in the scatterplot and where that time is represented in the Sankey diagram.
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Keywords

High dimensional data, Dynamic topic modeling, Cluster analysis

Abstract

Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

IEEE VIS 2024 Content: Visualizing Temporal Topic Embeddings with a Compass

Visualizing Temporal Topic Embeddings with a Compass

Daniel Palamarchuk - Virginia Tech, Blacksburg, United States

Lemara Williams - Virginia Polytechnic Institute of Technology , Blacksburg, United States

Brian Mayer - Virginia Tech, Blacksburg, United States

Thomas Danielson - Savannah River National Laboratory, Aiken, United States

Rebecca Faust - Tulane University, New Orleans, United States

Larry M Deschaine PhD - Savannah River National Laboratory, Aiken, United States

Chris North - Virginia Tech, Blacksburg, United States

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Room: Bayshore I

2024-10-17T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T12:30:00Z
Exemplar figure, described by caption below
We present the dynamic topic modeling method called Temporal Topic Embeddings with a Compass. The top-right image illustrates how this method effectively generates a plot of term movements within the context of documents and their associated topics. The outer image showcases TimeLink, a tool that compares word vectors in both global and local topic contexts. The red boxes correspond to the respective time periods: the time represented in the scatterplot and where that time is represented in the Sankey diagram.
Fast forward
Keywords

High dimensional data, Dynamic topic modeling, Cluster analysis

Abstract

Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

IEEE VIS 2024 Content: Blowing Seeds Across Gardens: Visualizing Implicit Propagation of Cross-Platform Social Media Posts

Blowing Seeds Across Gardens: Visualizing Implicit Propagation of Cross-Platform Social Media Posts

Jianing Yin - Zhejiang University, Hangzhou, China

Hanze Jia - Zhejiang University, Hangzhou, China

Buwei Zhou - Zhejiang University, Hangzhou, China

Tan Tang - Zhejiang University, Hangzhou, China

Lu Ying - Zhejiang University, Hangzhou, China

Shuainan Ye - Zhejiang University, Hangzhou, China

Tai-Quan Peng - Michigan State University, East Lansing, United States

Yingcai Wu - Zhejiang University, Hangzhou, China

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Room: Bayshore III

2024-10-17T18:33:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:33:00Z
Exemplar figure, described by caption below
Interface Overview of BloomWind (Cluster-level): (a) Cluster-level Propagation View, demonstrating the diffusion process of topics among platforms; (b) Timeline View, for selecting a time frame and controlling the animation process of propagation; (c) Cluster-level Detail View, listing the post and user information by topic and platform.
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Keywords

Propagation analysis, social media visualization, cross-platform propagation, metaphor design

Abstract

Propagation analysis refers to studying how information spreads on social media, a pivotal endeavor for understanding social sentiment and public opinions. Numerous studies contribute to visualizing information spread, but few have considered the implicit and complex diffusion patterns among multiple platforms. To bridge the gap, we summarize cross-platform diffusion patterns with experts and identify significant factors that dissect the mechanisms of cross-platform information spread. Based on that, we propose an information diffusion model that estimates the likelihood of a topic/post spreading among different social media platforms. Moreover, we propose a novel visual metaphor that encapsulates cross-platform propagation in a manner analogous to the spread of seeds across gardens. Specifically, we visualize platforms, posts, implicit cross-platform routes, and salient instances as elements of a virtual ecosystem — gardens, flowers, winds, and seeds, respectively. We further develop a visual analytic system, namely BloomWind, that enables users to quickly identify the cross-platform diffusion patterns and investigate the relevant social media posts. Ultimately, we demonstrate the usage of BloomWind through two case studies and validate its effectiveness using expert interviews.

IEEE VIS 2024 Content: Blowing Seeds Across Gardens: Visualizing Implicit Propagation of Cross-Platform Social Media Posts

Blowing Seeds Across Gardens: Visualizing Implicit Propagation of Cross-Platform Social Media Posts

Jianing Yin - Zhejiang University, Hangzhou, China

Hanze Jia - Zhejiang University, Hangzhou, China

Buwei Zhou - Zhejiang University, Hangzhou, China

Tan Tang - Zhejiang University, Hangzhou, China

Lu Ying - Zhejiang University, Hangzhou, China

Shuainan Ye - Zhejiang University, Hangzhou, China

Tai-Quan Peng - Michigan State University, East Lansing, United States

Yingcai Wu - Zhejiang University, Hangzhou, China

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Room: Bayshore III

2024-10-17T18:33:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:33:00Z
Exemplar figure, described by caption below
Interface Overview of BloomWind (Cluster-level): (a) Cluster-level Propagation View, demonstrating the diffusion process of topics among platforms; (b) Timeline View, for selecting a time frame and controlling the animation process of propagation; (c) Cluster-level Detail View, listing the post and user information by topic and platform.
Fast forward
Keywords

Propagation analysis, social media visualization, cross-platform propagation, metaphor design

Abstract

Propagation analysis refers to studying how information spreads on social media, a pivotal endeavor for understanding social sentiment and public opinions. Numerous studies contribute to visualizing information spread, but few have considered the implicit and complex diffusion patterns among multiple platforms. To bridge the gap, we summarize cross-platform diffusion patterns with experts and identify significant factors that dissect the mechanisms of cross-platform information spread. Based on that, we propose an information diffusion model that estimates the likelihood of a topic/post spreading among different social media platforms. Moreover, we propose a novel visual metaphor that encapsulates cross-platform propagation in a manner analogous to the spread of seeds across gardens. Specifically, we visualize platforms, posts, implicit cross-platform routes, and salient instances as elements of a virtual ecosystem — gardens, flowers, winds, and seeds, respectively. We further develop a visual analytic system, namely BloomWind, that enables users to quickly identify the cross-platform diffusion patterns and investigate the relevant social media posts. Ultimately, we demonstrate the usage of BloomWind through two case studies and validate its effectiveness using expert interviews.

IEEE VIS 2024 Content: DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer

DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer

Andrew Wentzel - University of Illinois at Chicago, Chicago, United States

Serageldin Attia - University of Houston, Houston, United States

Xinhua Zhang - University of Illinois Chicago, Chicago, United States

Guadalupe Canahuate - University of Iowa, Iowa City, United States

Clifton David Fuller - University of Texas, Houston, United States

G. Elisabeta Marai - University of Illinois at Chicago, Chicago, United States

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Room: Bayshore V

2024-10-17T18:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:45:00Z
Exemplar figure, described by caption below
Overview of DITTO. (A) Input panel to alter model parameters and input patient features. (B) Temporal outcome risk plots for the patient based on different models and treatment groups. (C) Treatment recommendation based on the twin model and similar patients. (D) Auxiliary data panel, currently showing a waterfall plot of how each feature cumulatively contributes to the model decision.
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Keywords

Medicine; Machine Learning; Application Domains; High Dimensional data; Spatial Data; Activity Centered Design

Abstract

Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk profiles necessitates a deeper understanding of the interplay between different factors such as patient health, spatial tumor location and spread, and risk of subsequent toxicities that can not be adequately captured through simple heuristics. To support clinicians in better understanding tradeoffs when deciding on treatment courses, we developed DITTO, a digital-twin and visual computing system that allows clinicians to analyze detailed risk profiles for each patient, and decide on a treatment plan. DITTO relies on a sequential Deep Reinforcement Learning digital twin (DT) to deliver personalized risk of both long-term and short-term disease outcome and toxicity risk for HNC patients. Based on a participatory collaborative design alongside oncologists, we also implement several visual explainability methods to promote clinical trust and encourage healthy skepticism when using our system. We evaluate the efficacy of DITTO through quantitative evaluation of performance and case studies with qualitative feedback. Finally, we discuss design lessons for developing clinical visual XAI applications for clinical end users.

IEEE VIS 2024 Content: DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer

DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer

Andrew Wentzel - University of Illinois at Chicago, Chicago, United States

Serageldin Attia - University of Houston, Houston, United States

Xinhua Zhang - University of Illinois Chicago, Chicago, United States

Guadalupe Canahuate - University of Iowa, Iowa City, United States

Clifton David Fuller - University of Texas, Houston, United States

G. Elisabeta Marai - University of Illinois at Chicago, Chicago, United States

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Room: Bayshore V

2024-10-17T18:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:45:00Z
Exemplar figure, described by caption below
Overview of DITTO. (A) Input panel to alter model parameters and input patient features. (B) Temporal outcome risk plots for the patient based on different models and treatment groups. (C) Treatment recommendation based on the twin model and similar patients. (D) Auxiliary data panel, currently showing a waterfall plot of how each feature cumulatively contributes to the model decision.
Fast forward
Keywords

Medicine; Machine Learning; Application Domains; High Dimensional data; Spatial Data; Activity Centered Design

Abstract

Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk profiles necessitates a deeper understanding of the interplay between different factors such as patient health, spatial tumor location and spread, and risk of subsequent toxicities that can not be adequately captured through simple heuristics. To support clinicians in better understanding tradeoffs when deciding on treatment courses, we developed DITTO, a digital-twin and visual computing system that allows clinicians to analyze detailed risk profiles for each patient, and decide on a treatment plan. DITTO relies on a sequential Deep Reinforcement Learning digital twin (DT) to deliver personalized risk of both long-term and short-term disease outcome and toxicity risk for HNC patients. Based on a participatory collaborative design alongside oncologists, we also implement several visual explainability methods to promote clinical trust and encourage healthy skepticism when using our system. We evaluate the efficacy of DITTO through quantitative evaluation of performance and case studies with qualitative feedback. Finally, we discuss design lessons for developing clinical visual XAI applications for clinical end users.

IEEE VIS 2024 Content: From Instruction to Insight: Exploring the Semantic and Functional Roles of Text in Interactive Dashboards

Honorable Mention

From Instruction to Insight: Exploring the Semantic and Functional Roles of Text in Interactive Dashboards

Nicole Sultanum - Tableau Research, Seattle, United States

Vidya Setlur - Tableau Research, Palo Alto, United States

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Room: Bayshore V

2024-10-16T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T13:06:00Z
Exemplar figure, described by caption below
Our work seeks to elevate text as a first-class citizen in dashboards. From a survey and analysis of 190 dashboards and interview feedback from 13 experts, we (a) highlight current dashboard text practices, (b) propose and validate recommended practices as a set of 12 heuristics for dashboard text, and (c) outline opportunities for future research to take dashboard text to the next level.
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Keywords

Text, dashboards, semantic levels, metadata, interactivity, instruction, description, takeaways, conversational heuristics

Abstract

There is increased interest in understanding the interplay between text and visuals in the field of data visualization. However, this attention has predominantly been on the use of text in standalone visualizations (such as text annotation overlays) or augmenting text stories supported by a series of independent views. In this paper, we shift from the traditional focus on single-chart annotations to characterize the nuanced but crucial communication role of text in the complex environment of interactive dashboards. Through a survey and analysis of 190 dashboards in the wild, plus 13 expert interview sessions with experienced dashboard authors, we highlight the distinctive nature of text as an integral component of the dashboard experience, while delving into the categories, semantic levels, and functional roles of text, and exploring how these text elements are coalesced by dashboard authors to guide and inform dashboard users. Our contributions are threefold. First, we distill qualitative and quantitative findings from our studies to characterize current practices of text use in dashboards, including a categorization of text-based components and design patterns. Second, we leverage current practices and existing literature to propose, discuss, and validate recommended practices for text in dashboards, embodied as a set of 12 heuristics that underscore the semantic and functional role of text in offering navigational cues, contextualizing data insights, supporting reading order, among other concerns. Third, we reflect on our findings to identify gaps and propose opportunities for data visualization researchers to push the boundaries on text usage for dashboards, from authoring support and interactivity to text generation and content personalization. Our research underscores the significance of elevating text as a first-class citizen in data visualization, and the need to support the inclusion of textual components and their interactive affordances in dashboard design.

IEEE VIS 2024 Content: From Instruction to Insight: Exploring the Semantic and Functional Roles of Text in Interactive Dashboards

Honorable Mention

From Instruction to Insight: Exploring the Semantic and Functional Roles of Text in Interactive Dashboards

Nicole Sultanum - Tableau Research, Seattle, United States

Vidya Setlur - Tableau Research, Palo Alto, United States

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Room: Bayshore V

2024-10-16T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T13:06:00Z
Exemplar figure, described by caption below
Our work seeks to elevate text as a first-class citizen in dashboards. From a survey and analysis of 190 dashboards and interview feedback from 13 experts, we (a) highlight current dashboard text practices, (b) propose and validate recommended practices as a set of 12 heuristics for dashboard text, and (c) outline opportunities for future research to take dashboard text to the next level.
Fast forward
Keywords

Text, dashboards, semantic levels, metadata, interactivity, instruction, description, takeaways, conversational heuristics

Abstract

There is increased interest in understanding the interplay between text and visuals in the field of data visualization. However, this attention has predominantly been on the use of text in standalone visualizations (such as text annotation overlays) or augmenting text stories supported by a series of independent views. In this paper, we shift from the traditional focus on single-chart annotations to characterize the nuanced but crucial communication role of text in the complex environment of interactive dashboards. Through a survey and analysis of 190 dashboards in the wild, plus 13 expert interview sessions with experienced dashboard authors, we highlight the distinctive nature of text as an integral component of the dashboard experience, while delving into the categories, semantic levels, and functional roles of text, and exploring how these text elements are coalesced by dashboard authors to guide and inform dashboard users. Our contributions are threefold. First, we distill qualitative and quantitative findings from our studies to characterize current practices of text use in dashboards, including a categorization of text-based components and design patterns. Second, we leverage current practices and existing literature to propose, discuss, and validate recommended practices for text in dashboards, embodied as a set of 12 heuristics that underscore the semantic and functional role of text in offering navigational cues, contextualizing data insights, supporting reading order, among other concerns. Third, we reflect on our findings to identify gaps and propose opportunities for data visualization researchers to push the boundaries on text usage for dashboards, from authoring support and interactivity to text generation and content personalization. Our research underscores the significance of elevating text as a first-class citizen in data visualization, and the need to support the inclusion of textual components and their interactive affordances in dashboard design.

IEEE VIS 2024 Content: DeLVE into Earth’s Past: A Visualization-Based Exhibit Deployed Across Multiple Museum Contexts

DeLVE into Earth’s Past: A Visualization-Based Exhibit Deployed Across Multiple Museum Contexts

Mara Solen - The University of British Columbia, Vancouver, Canada

Nigar Sultana - University of British Columbia , Vancouver, Canada

Laura A. Lukes - University of British Columbia, Vancouver, Canada

Tamara Munzner - University of British Columbia, Vancouver, Canada

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Room: Bayshore V

2024-10-17T16:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:00:00Z
Exemplar figure, described by caption below
The DeLVE visualization software, displaying the dataset of past events in biological and geological history, as deployed at a biology museum. The data is visualized across multiple scales using our novel Connected Multi-Tier Ranges idiom.
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Keywords

Visualization, design study, museum, deep time.

Abstract

While previous work has found success in deploying visualizations as museum exhibits, it has not investigated whether museum context impacts visitor behaviour with these exhibits. We present an interactive Deep-time Literacy Visualization Exhibit (DeLVE) to help museum visitors understand deep time (lengths of extremely long geological processes) by improving proportional reasoning skills through comparison of different time periods. DeLVE uses a new visualization idiom, Connected Multi-Tier Ranges, to visualize curated datasets of past events across multiple scales of time, relating extreme scales with concrete scales that have more familiar magnitudes and units. Museum staff at three separate museums approved the deployment of DeLVE as a digital kiosk, and devoted time to curating a unique dataset in each of them. We collect data from two sources, an observational study and system trace logs. We discuss the importance of context: similar museum exhibits in different contexts were received very differently by visitors. We additionally discuss differences in our process from Sedlmair et al.'s design study methodology which is focused on design studies triggered by connection with collaborators rather than the discovery of a concept to communicate. Supplemental materials are available at: https://osf.io/z53dq/

IEEE VIS 2024 Content: DeLVE into Earth’s Past: A Visualization-Based Exhibit Deployed Across Multiple Museum Contexts

DeLVE into Earth’s Past: A Visualization-Based Exhibit Deployed Across Multiple Museum Contexts

Mara Solen - The University of British Columbia, Vancouver, Canada

Nigar Sultana - University of British Columbia , Vancouver, Canada

Laura A. Lukes - University of British Columbia, Vancouver, Canada

Tamara Munzner - University of British Columbia, Vancouver, Canada

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Room: Bayshore V

2024-10-17T16:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:00:00Z
Exemplar figure, described by caption below
The DeLVE visualization software, displaying the dataset of past events in biological and geological history, as deployed at a biology museum. The data is visualized across multiple scales using our novel Connected Multi-Tier Ranges idiom.
Fast forward
Keywords

Visualization, design study, museum, deep time.

Abstract

While previous work has found success in deploying visualizations as museum exhibits, it has not investigated whether museum context impacts visitor behaviour with these exhibits. We present an interactive Deep-time Literacy Visualization Exhibit (DeLVE) to help museum visitors understand deep time (lengths of extremely long geological processes) by improving proportional reasoning skills through comparison of different time periods. DeLVE uses a new visualization idiom, Connected Multi-Tier Ranges, to visualize curated datasets of past events across multiple scales of time, relating extreme scales with concrete scales that have more familiar magnitudes and units. Museum staff at three separate museums approved the deployment of DeLVE as a digital kiosk, and devoted time to curating a unique dataset in each of them. We collect data from two sources, an observational study and system trace logs. We discuss the importance of context: similar museum exhibits in different contexts were received very differently by visitors. We additionally discuss differences in our process from Sedlmair et al.'s design study methodology which is focused on design studies triggered by connection with collaborators rather than the discovery of a concept to communicate. Supplemental materials are available at: https://osf.io/z53dq/

IEEE VIS 2024 Content: AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow

Honorable Mention

AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow

Dazhen Deng - Zhejiang University, Ningbo, China

Chuhan Zhang - Zhejiang University, Hangzhou, China

Huawei Zheng - Zhejiang University, Hangzhou, China

Yuwen Pu - Zhejiang University, Hangzhou, China

Shouling Ji - Zhejiang University, Hangzhou, China

Yingcai Wu - Zhejiang University, Hangzhou, China

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Room: Bayshore V

2024-10-18T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:30:00Z
Exemplar figure, described by caption below
The interface of AdversaFlow includes a Control Panel (A) to configure model parameters and adjust data sampling, an Embedding View (B) to show the projection of prompts, a Metric Monitor (C) displaying the key performance indicators of the model, an Adversarial Flow to facilitate multi-level exploration of models, an Instance List (E) showing prompt details, and a Flucutaion View (F) for the investigation of token-level uncertainty.
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Keywords

Visual Analytics for Machine Learning, Artificial Intelligence Security, Large Language Models, Text Visualization

Abstract

Large Language Models (LLMs) are powerful but also raise significant security concerns, particularly regarding the harm they can cause, such as generating fake news that manipulates public opinion on social media and providing responses to unethical activities. Traditional red teaming approaches for identifying AI vulnerabilities rely on manual prompt construction and expertise. This paper introduces AdversaFlow, a novel visual analytics system designed to enhance LLM security against adversarial attacks through human-AI collaboration. AdversaFlow involves adversarial training between a target model and a red model, featuring unique multi-level adversarial flow and fluctuation path visualizations. These features provide insights into adversarial dynamics and LLM robustness, enabling experts to identify and mitigate vulnerabilities effectively. We present quantitative evaluations and case studies validating our system's utility and offering insights for future AI security solutions. Our method can enhance LLM security, supporting downstream scenarios like social media regulation by enabling more effective detection, monitoring, and mitigation of harmful content and behaviors.

IEEE VIS 2024 Content: AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow

Honorable Mention

AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow

Dazhen Deng - Zhejiang University, Ningbo, China

Chuhan Zhang - Zhejiang University, Hangzhou, China

Huawei Zheng - Zhejiang University, Hangzhou, China

Yuwen Pu - Zhejiang University, Hangzhou, China

Shouling Ji - Zhejiang University, Hangzhou, China

Yingcai Wu - Zhejiang University, Hangzhou, China

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Room: Bayshore V

2024-10-18T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:30:00Z
Exemplar figure, described by caption below
The interface of AdversaFlow includes a Control Panel (A) to configure model parameters and adjust data sampling, an Embedding View (B) to show the projection of prompts, a Metric Monitor (C) displaying the key performance indicators of the model, an Adversarial Flow to facilitate multi-level exploration of models, an Instance List (E) showing prompt details, and a Flucutaion View (F) for the investigation of token-level uncertainty.
Fast forward
Keywords

Visual Analytics for Machine Learning, Artificial Intelligence Security, Large Language Models, Text Visualization

Abstract

Large Language Models (LLMs) are powerful but also raise significant security concerns, particularly regarding the harm they can cause, such as generating fake news that manipulates public opinion on social media and providing responses to unethical activities. Traditional red teaming approaches for identifying AI vulnerabilities rely on manual prompt construction and expertise. This paper introduces AdversaFlow, a novel visual analytics system designed to enhance LLM security against adversarial attacks through human-AI collaboration. AdversaFlow involves adversarial training between a target model and a red model, featuring unique multi-level adversarial flow and fluctuation path visualizations. These features provide insights into adversarial dynamics and LLM robustness, enabling experts to identify and mitigate vulnerabilities effectively. We present quantitative evaluations and case studies validating our system's utility and offering insights for future AI security solutions. Our method can enhance LLM security, supporting downstream scenarios like social media regulation by enabling more effective detection, monitoring, and mitigation of harmful content and behaviors.

IEEE VIS 2024 Content: Entanglements for Visualization: Changing Research Outcomes through Feminist Theory

Best Paper Award

Entanglements for Visualization: Changing Research Outcomes through Feminist Theory

Derya Akbaba - Linköping University, Norrköping, Sweden

Lauren Klein - Emory University, Atlanta, United States

Miriah Meyer - Linköping University, Nörrkoping, Sweden

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Room: Bayshore I + II + III

2024-10-15T16:10:00Z GMT-0600 Change your timezone on the schedule page
2024-10-15T16:10:00Z
Exemplar figure, described by caption below
A series of overlapping circles that are made up of four concentric circles. The inner circle is labeled the knowledge artifact, then entanglements with phenomenon, then entanglements with apparatus, then entanglements. These concentric circles overlap in a wave of entanglements and cover topics listed as: data, vis, insight, power, conventions, technology, history, processes, materiality, people, society, design, labor, politics, ethics, places.
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Keywords

Epistemology, feminism, entanglement, theory

Abstract

A growing body of work draws on feminist thinking to challenge assumptions about how people engage with and use visualizations. This work draws on feminist values, driving design and research guidelines that account for the influences of power and neglect. This prior work is largely prescriptive, however, forgoing articulation of how feminist theories of knowledge — or feminist epistemology — can alter research design and outcomes. At the core of our work is an engagement with feminist epistemology, drawing attention to how a new framework for how we know what we know enabled us to overcome intellectual tensions in our research. Specifically, we focus on the theoretical concept of entanglement, central to recent feminist scholarship, and contribute: a history of entanglement in the broader scope of feminist theory; an articulation of the main points of entanglement theory for a visualization context; and a case study of research outcomes as evidence of the potential of feminist epistemology to impact visualization research. This work answers a call in the community to embrace a broader set of theoretical and epistemic foundations and provides a starting point for bringing feminist theories into visualization research.

IEEE VIS 2024 Content: Entanglements for Visualization: Changing Research Outcomes through Feminist Theory

Best Paper Award

Entanglements for Visualization: Changing Research Outcomes through Feminist Theory

Derya Akbaba - Linköping University, Norrköping, Sweden

Lauren Klein - Emory University, Atlanta, United States

Miriah Meyer - Linköping University, Nörrkoping, Sweden

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Room: Bayshore I + II + III

2024-10-15T16:10:00Z GMT-0600 Change your timezone on the schedule page
2024-10-15T16:10:00Z
Exemplar figure, described by caption below
A series of overlapping circles that are made up of four concentric circles. The inner circle is labeled the knowledge artifact, then entanglements with phenomenon, then entanglements with apparatus, then entanglements. These concentric circles overlap in a wave of entanglements and cover topics listed as: data, vis, insight, power, conventions, technology, history, processes, materiality, people, society, design, labor, politics, ethics, places.
Fast forward
Keywords

Epistemology, feminism, entanglement, theory

Abstract

A growing body of work draws on feminist thinking to challenge assumptions about how people engage with and use visualizations. This work draws on feminist values, driving design and research guidelines that account for the influences of power and neglect. This prior work is largely prescriptive, however, forgoing articulation of how feminist theories of knowledge — or feminist epistemology — can alter research design and outcomes. At the core of our work is an engagement with feminist epistemology, drawing attention to how a new framework for how we know what we know enabled us to overcome intellectual tensions in our research. Specifically, we focus on the theoretical concept of entanglement, central to recent feminist scholarship, and contribute: a history of entanglement in the broader scope of feminist theory; an articulation of the main points of entanglement theory for a visualization context; and a case study of research outcomes as evidence of the potential of feminist epistemology to impact visualization research. This work answers a call in the community to embrace a broader set of theoretical and epistemic foundations and provides a starting point for bringing feminist theories into visualization research.

IEEE VIS 2024 Content: Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

Lin Gao - Fudan University, Shanghai, China

Jing Lu - Fudan University, ShangHai, China

Zekai Shao - Fudan University, Shanghai, China

Ziyue Lin - Fudan University, Shanghai, China

Shengbin Yue - Fudan unversity, ShangHai, China

Chiokit Ieong - Fudan University, Shanghai, China

Yi Sun - Fudan University, Shanghai, China

Rory Zauner - University of Vienna, Vienna, Austria

Zhongyu Wei - Fudan University, Shanghai, China

Siming Chen - Fudan University, Shanghai, China

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Room: Bayshore V

2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
In applying workflow to Self-Regulated Learning (SRL) in education, we outline the process in three phases. Phase 1 involves establishing a fundamental understanding of the SRL task (A1) and collecting data on artificial intelligence (A2). The design requirements (B) align with the design requirements. Phase 2 details the SRL pipeline sub-tasks and visualizations (C1), leading to the creation of fine-tuning data (C2). In phase 3, we enhance the fine-tuning effects and visualization interactions by integrating user feedback within the visualization system.
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Keywords

Fine-tuned large language model, visualization system, self-regulated learning, intelligent tutorial system

Abstract

Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework.

IEEE VIS 2024 Content: Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

Lin Gao - Fudan University, Shanghai, China

Jing Lu - Fudan University, ShangHai, China

Zekai Shao - Fudan University, Shanghai, China

Ziyue Lin - Fudan University, Shanghai, China

Shengbin Yue - Fudan unversity, ShangHai, China

Chiokit Ieong - Fudan University, Shanghai, China

Yi Sun - Fudan University, Shanghai, China

Rory Zauner - University of Vienna, Vienna, Austria

Zhongyu Wei - Fudan University, Shanghai, China

Siming Chen - Fudan University, Shanghai, China

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Room: Bayshore V

2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
In applying workflow to Self-Regulated Learning (SRL) in education, we outline the process in three phases. Phase 1 involves establishing a fundamental understanding of the SRL task (A1) and collecting data on artificial intelligence (A2). The design requirements (B) align with the design requirements. Phase 2 details the SRL pipeline sub-tasks and visualizations (C1), leading to the creation of fine-tuning data (C2). In phase 3, we enhance the fine-tuning effects and visualization interactions by integrating user feedback within the visualization system.
Fast forward
Keywords

Fine-tuned large language model, visualization system, self-regulated learning, intelligent tutorial system

Abstract

Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework.

IEEE VIS 2024 Content: Smartboard: Visual Exploration of Team Tactics with LLM Agent

Smartboard: Visual Exploration of Team Tactics with LLM Agent

Ziao Liu - Zhejiang University, Hangzhou, China

Xiao Xie - Zhejiang University, Hangzhou, China

Moqi He - Zhejiang University, Hangzhou, China

Wenshuo Zhao - Zhejiang University, Hangzhou, China

Yihong Wu - Zhejiang University, Hangzhou, China

Liqi Cheng - Zhejiang University, Hangzhou, China

Hui Zhang - Zhejiang University, Hangzhou, China

Yingcai Wu - Zhejiang University, Hangzhou, China

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Room: Bayshore V

2024-10-17T14:39:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T14:39:00Z
Exemplar figure, described by caption below
The system interface of Smartboard. (A) The chat view provides system feedback and enhances communication between users and the system through tag selections and open-question answering. (B) The setup view provides interactions during tactical setup with tactics sketching, matchup analysis, and situation retrieval. (C) The simulation view presents the coach agent's recommended tactics, along with explanations and evaluations in both overview and detail. (D) The history view records users' tactics and provides the classic tactics for starting exploration.
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Keywords

Sports visualization, tactic board, tactical analysis

Abstract

Tactics play an important role in team sports by guiding how players interact on the field. Both sports fans and experts have a demand for analyzing sports tactics. Existing approaches allow users to visually perceive the multivariate tactical effects. However, these approaches require users to experience a complex reasoning process to connect the multiple interactions within each tactic to the final tactical effect. In this work, we collaborate with basketball experts and propose a progressive approach to help users gain a deeper understanding of how each tactic works and customize tactics on demand. Users can progressively sketch on a tactic board, and a coach agent will simulate the possible actions in each step and present the simulation to users with facet visualizations. We develop an extensible framework that integrates large language models (LLMs) and visualizations to help users communicate with the coach agent with multimodal inputs. Based on the framework, we design and develop Smartboard, an agent-based interactive visualization system for fine-grained tactical analysis, especially for play design. Smartboard provides users with a structured process of setup, simulation, and evolution, allowing for iterative exploration of tactics based on specific personalized scenarios. We conduct case studies based on real-world basketball datasets to demonstrate the effectiveness and usefulness of our system.

IEEE VIS 2024 Content: Smartboard: Visual Exploration of Team Tactics with LLM Agent

Smartboard: Visual Exploration of Team Tactics with LLM Agent

Ziao Liu - Zhejiang University, Hangzhou, China

Xiao Xie - Zhejiang University, Hangzhou, China

Moqi He - Zhejiang University, Hangzhou, China

Wenshuo Zhao - Zhejiang University, Hangzhou, China

Yihong Wu - Zhejiang University, Hangzhou, China

Liqi Cheng - Zhejiang University, Hangzhou, China

Hui Zhang - Zhejiang University, Hangzhou, China

Yingcai Wu - Zhejiang University, Hangzhou, China

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Room: Bayshore V

2024-10-17T14:39:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T14:39:00Z
Exemplar figure, described by caption below
The system interface of Smartboard. (A) The chat view provides system feedback and enhances communication between users and the system through tag selections and open-question answering. (B) The setup view provides interactions during tactical setup with tactics sketching, matchup analysis, and situation retrieval. (C) The simulation view presents the coach agent's recommended tactics, along with explanations and evaluations in both overview and detail. (D) The history view records users' tactics and provides the classic tactics for starting exploration.
Fast forward
Keywords

Sports visualization, tactic board, tactical analysis

Abstract

Tactics play an important role in team sports by guiding how players interact on the field. Both sports fans and experts have a demand for analyzing sports tactics. Existing approaches allow users to visually perceive the multivariate tactical effects. However, these approaches require users to experience a complex reasoning process to connect the multiple interactions within each tactic to the final tactical effect. In this work, we collaborate with basketball experts and propose a progressive approach to help users gain a deeper understanding of how each tactic works and customize tactics on demand. Users can progressively sketch on a tactic board, and a coach agent will simulate the possible actions in each step and present the simulation to users with facet visualizations. We develop an extensible framework that integrates large language models (LLMs) and visualizations to help users communicate with the coach agent with multimodal inputs. Based on the framework, we design and develop Smartboard, an agent-based interactive visualization system for fine-grained tactical analysis, especially for play design. Smartboard provides users with a structured process of setup, simulation, and evolution, allowing for iterative exploration of tactics based on specific personalized scenarios. We conduct case studies based on real-world basketball datasets to demonstrate the effectiveness and usefulness of our system.

IEEE VIS 2024 Content: Causal Priors and Their Influence on Judgements of Causality in Visualized Data

Causal Priors and Their Influence on Judgements of Causality in Visualized Data

Arran Zeyu Wang - University of North Carolina-Chapel Hill, Chapel Hill, United States

David Borland - UNC-Chapel Hill, Chapel Hill, United States

Tabitha C. Peck - Davidson College, Davidson, United States

Wenyuan Wang - University of North Carolina, Chapel Hill, United States

David Gotz - University of North Carolina, Chapel Hill, United States

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Room: Bayshore II

2024-10-16T14:51:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:51:00Z
Exemplar figure, described by caption below
Results of participant-rated causal relationships for 56 concept pairs from open-source datasets. Participants rated the causal impact of X on Y for each pair on a scale of 1 to 5. The Y-axis in (a) shows these scores, ordered by mean causal relation on the X-axis with 95% confidence intervals. The light blue band represents the mean score +/- one standard deviation (SD). Vertical dashed lines indicate low (<mean-SD) and high (>mean+SD) causal priors. (b) presents heat maps for four example pairs, showing participant scores. The study highlights the variability in causal priors and their impact on visualization interpretation.
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Keywords

Causal inference, Perception and cognition, Causal prior, Association, Causality, Visualization

Abstract

``Correlation does not imply causation'' is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations. We collected a corpus of concept pairs from variables in widely used datasets and created visualizations that depict varying correlative associations using three typical statistical chart types. We conducted two MTurk studies on (1) preconceived notions on causal relations without charts, and (2) perceived causal relations with charts, for each concept pair. Our results indicate that people make assumptions about causal relationships between pairs of concepts even without seeing any visualized data. Moreover, our results suggest that these assumptions constitute causal priors that, in combination with visualized association, impact how data visualizations are interpreted. The results also suggest that causal priors may lead to over- or under-estimation in perceived causal relations in different circumstances, and that those priors can also impact users' confidence in their causal assessments. In addition, our results align with prior work, indicating that chart type may also affect causal inference. Using data from the studies, we develop a model to capture the interaction between causal priors and visualized associations as they combine to impact a user's perceived causal relations. In addition to reporting the study results and analyses, we provide an open dataset of causal priors for 56 specific concept pairs that can serve as a potential benchmark for future studies. We also suggest remaining challenges and heuristic-based guidelines to help designers improve visualization design choices to better support visual causal inference.

IEEE VIS 2024 Content: Causal Priors and Their Influence on Judgements of Causality in Visualized Data

Causal Priors and Their Influence on Judgements of Causality in Visualized Data

Arran Zeyu Wang - University of North Carolina-Chapel Hill, Chapel Hill, United States

David Borland - UNC-Chapel Hill, Chapel Hill, United States

Tabitha C. Peck - Davidson College, Davidson, United States

Wenyuan Wang - University of North Carolina, Chapel Hill, United States

David Gotz - University of North Carolina, Chapel Hill, United States

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Room: Bayshore II

2024-10-16T14:51:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:51:00Z
Exemplar figure, described by caption below
Results of participant-rated causal relationships for 56 concept pairs from open-source datasets. Participants rated the causal impact of X on Y for each pair on a scale of 1 to 5. The Y-axis in (a) shows these scores, ordered by mean causal relation on the X-axis with 95% confidence intervals. The light blue band represents the mean score +/- one standard deviation (SD). Vertical dashed lines indicate low (<mean-SD) and high (>mean+SD) causal priors. (b) presents heat maps for four example pairs, showing participant scores. The study highlights the variability in causal priors and their impact on visualization interpretation.
Fast forward
Keywords

Causal inference, Perception and cognition, Causal prior, Association, Causality, Visualization

Abstract

``Correlation does not imply causation'' is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations. We collected a corpus of concept pairs from variables in widely used datasets and created visualizations that depict varying correlative associations using three typical statistical chart types. We conducted two MTurk studies on (1) preconceived notions on causal relations without charts, and (2) perceived causal relations with charts, for each concept pair. Our results indicate that people make assumptions about causal relationships between pairs of concepts even without seeing any visualized data. Moreover, our results suggest that these assumptions constitute causal priors that, in combination with visualized association, impact how data visualizations are interpreted. The results also suggest that causal priors may lead to over- or under-estimation in perceived causal relations in different circumstances, and that those priors can also impact users' confidence in their causal assessments. In addition, our results align with prior work, indicating that chart type may also affect causal inference. Using data from the studies, we develop a model to capture the interaction between causal priors and visualized associations as they combine to impact a user's perceived causal relations. In addition to reporting the study results and analyses, we provide an open dataset of causal priors for 56 specific concept pairs that can serve as a potential benchmark for future studies. We also suggest remaining challenges and heuristic-based guidelines to help designers improve visualization design choices to better support visual causal inference.

IEEE VIS 2024 Content: PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets

PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets

Jaeyoung Kim - Seoul National University, Seoul, Korea, Republic of

Sihyeon Lee - Seoul National University, Seoul, Korea, Republic of

Hyeon Jeon - Seoul National University, Seoul, Korea, Republic of

Keon-Joo Lee - Korea University Guro Hospital, Seoul, Korea, Republic of

Bohyoung Kim - Hankuk University of Foreign Studies, Yongin-si, Korea, Republic of

HEE JOON - Seoul National University Bundang Hospital, Seongnam, Korea, Republic of

Jinwook Seo - Seoul National University, Seoul, Korea, Republic of

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Room: Bayshore I

2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:12:00Z
Exemplar figure, described by caption below
PhenoFlow empowers neurologists to explore large and complex stroke datasets with reduced cognitive load. (A) The cohort construction component allows neurologists to define target cohorts using natural language. (B) The Visual Inspection View provides plain-language explanations and small multiples of relevant fields to debug LLM data wrangler behavior. (C) The Cohort View summarizes (C1) cohort relationships in a node-link diagram and (C2) each patient's blood pressure (BP) trajectories as matrix visualization. (C3) Natural language filtering supports iterative cohort exploration. (D1) Linear bar charts and (D2) slice-and-wrap visualization present BP trajectories as time-series, revealing triangular patterns in irregularly measured BP data.
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Keywords

Stroke, Irregularly spaced time-series data, Multi-dimensional data, Cohort analysis, Large language models

Abstract

Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial obstacles to effective visual analytics and decision-making. Through a year-long collaboration with experienced neurologists, we developed PhenoFlow, a visual analytics system that leverages the collaboration between human and Large Language Models (LLMs) to analyze the extensive and complex data of acute ischemic stroke patients. PhenoFlow pioneers an innovative workflow, where the LLM serves as a data wrangler while neurologists explore and supervise the output using visualizations and natural language interactions. This approach enables neurologists to focus more on decision-making with reduced cognitive load. To protect sensitive patient information, PhenoFlow only utilizes metadata to make inferences and synthesize executable codes, without accessing raw patient data. This ensures that the results are both reproducible and interpretable while maintaining patient privacy. The system incorporates a slice-and-wrap design that employs temporal folding to create an overlaid circular visualization. Combined with a linear bar graph, this design aids in exploring meaningful patterns within irregularly measured BP data. Through case studies, PhenoFlow has demonstrated its capability to support iterative analysis of extensive clinical datasets, reducing cognitive load and enabling neurologists to make well-informed decisions. Grounded in long-term collaboration with domain experts, our research demonstrates the potential of utilizing LLMs to tackle current challenges in data-driven clinical decision-making for acute ischemic stroke patients.

IEEE VIS 2024 Content: PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets

PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets

Jaeyoung Kim - Seoul National University, Seoul, Korea, Republic of

Sihyeon Lee - Seoul National University, Seoul, Korea, Republic of

Hyeon Jeon - Seoul National University, Seoul, Korea, Republic of

Keon-Joo Lee - Korea University Guro Hospital, Seoul, Korea, Republic of

Bohyoung Kim - Hankuk University of Foreign Studies, Yongin-si, Korea, Republic of

HEE JOON - Seoul National University Bundang Hospital, Seongnam, Korea, Republic of

Jinwook Seo - Seoul National University, Seoul, Korea, Republic of

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Room: Bayshore I

2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:12:00Z
Exemplar figure, described by caption below
PhenoFlow empowers neurologists to explore large and complex stroke datasets with reduced cognitive load. (A) The cohort construction component allows neurologists to define target cohorts using natural language. (B) The Visual Inspection View provides plain-language explanations and small multiples of relevant fields to debug LLM data wrangler behavior. (C) The Cohort View summarizes (C1) cohort relationships in a node-link diagram and (C2) each patient's blood pressure (BP) trajectories as matrix visualization. (C3) Natural language filtering supports iterative cohort exploration. (D1) Linear bar charts and (D2) slice-and-wrap visualization present BP trajectories as time-series, revealing triangular patterns in irregularly measured BP data.
Fast forward
Keywords

Stroke, Irregularly spaced time-series data, Multi-dimensional data, Cohort analysis, Large language models

Abstract

Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial obstacles to effective visual analytics and decision-making. Through a year-long collaboration with experienced neurologists, we developed PhenoFlow, a visual analytics system that leverages the collaboration between human and Large Language Models (LLMs) to analyze the extensive and complex data of acute ischemic stroke patients. PhenoFlow pioneers an innovative workflow, where the LLM serves as a data wrangler while neurologists explore and supervise the output using visualizations and natural language interactions. This approach enables neurologists to focus more on decision-making with reduced cognitive load. To protect sensitive patient information, PhenoFlow only utilizes metadata to make inferences and synthesize executable codes, without accessing raw patient data. This ensures that the results are both reproducible and interpretable while maintaining patient privacy. The system incorporates a slice-and-wrap design that employs temporal folding to create an overlaid circular visualization. Combined with a linear bar graph, this design aids in exploring meaningful patterns within irregularly measured BP data. Through case studies, PhenoFlow has demonstrated its capability to support iterative analysis of extensive clinical datasets, reducing cognitive load and enabling neurologists to make well-informed decisions. Grounded in long-term collaboration with domain experts, our research demonstrates the potential of utilizing LLMs to tackle current challenges in data-driven clinical decision-making for acute ischemic stroke patients.

IEEE VIS 2024 Content: PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings

PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings

Fabian Beck - University of Bamberg, Bamberg, Germany

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Room: Bayshore I

2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:18:00Z
Exemplar figure, described by caption below
The figure showcases the PUREsuggest interface, a tool designed for citation-based literature search and visual exploration. The interface includes three main components: a list of currently selected publications, a list of suggested publications based on citation links, and a visualization of the citation network. Users can refine searches by adding publications and entering custom keywords to amplify specific research topics, facilitating an interactive and dynamic approach to discovering relevant literature.
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Keywords

Scientific literature search, citation network visualization, visual recommender system.

Abstract

Citations allow quickly identifying related research. If multiple publications are selected as seeds, specific suggestions for related literature can be made based on the number of incoming and outgoing citation links to this selection. Interactively adding recommended publications to the selection refines the next suggestion and incrementally builds a relevant collection of publications. Following this approach, the paper presents a search and foraging approach, PUREsuggest, which combines citation-based suggestions with augmented visualizations of the citation network. The focus and novelty of the approach is, first, the transparency of how the rankings are explained visually and, second, that the process can be steered through user-defined keywords, which reflect topics of interests. The system can be used to build new literature collections, to update and assess existing ones, as well as to use the collected literature for identifying relevant experts in the field. We evaluated the recommendation approach through simulated sessions and performed a user study investigating search strategies and usage patterns supported by the interface.

IEEE VIS 2024 Content: PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings

PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings

Fabian Beck - University of Bamberg, Bamberg, Germany

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Room: Bayshore I

2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:18:00Z
Exemplar figure, described by caption below
The figure showcases the PUREsuggest interface, a tool designed for citation-based literature search and visual exploration. The interface includes three main components: a list of currently selected publications, a list of suggested publications based on citation links, and a visualization of the citation network. Users can refine searches by adding publications and entering custom keywords to amplify specific research topics, facilitating an interactive and dynamic approach to discovering relevant literature.
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Keywords

Scientific literature search, citation network visualization, visual recommender system.

Abstract

Citations allow quickly identifying related research. If multiple publications are selected as seeds, specific suggestions for related literature can be made based on the number of incoming and outgoing citation links to this selection. Interactively adding recommended publications to the selection refines the next suggestion and incrementally builds a relevant collection of publications. Following this approach, the paper presents a search and foraging approach, PUREsuggest, which combines citation-based suggestions with augmented visualizations of the citation network. The focus and novelty of the approach is, first, the transparency of how the rankings are explained visually and, second, that the process can be steered through user-defined keywords, which reflect topics of interests. The system can be used to build new literature collections, to update and assess existing ones, as well as to use the collected literature for identifying relevant experts in the field. We evaluated the recommendation approach through simulated sessions and performed a user study investigating search strategies and usage patterns supported by the interface.

IEEE VIS 2024 Content: Touching the Ground: Evaluating the Effectiveness of Data Physicalizations for Spatial Data Analysis Tasks

Honorable Mention

Touching the Ground: Evaluating the Effectiveness of Data Physicalizations for Spatial Data Analysis Tasks

Bridger Herman - University of Minnesota, Minneapolis, United States

Cullen D. Jackson - Beth Israel Deaconess Medical Center, Boston, United States

Daniel F. Keefe - University of Minnesota, Minneapolis, United States

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Room: Bayshore I

2024-10-17T18:21:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:21:00Z
Exemplar figure, described by caption below
Data physicalizations provide many potential benefits over digital data displays, including haptic perception and body-centric judgments. This paper compares the effectiveness of physicalizations (left) with virtual reality (right top) and 2D visualizations (right bottom) for spatial data analysis tasks on digital elevation data common in climate science and natural resource management.
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Keywords

Data physicalization, virtual reality, evaluation.

Abstract

Abstract—Inspired by recent advances in digital fabrication, artists and scientists have demonstrated that physical data encodings (i.e., data physicalizations) can increase engagement with data, foster collaboration, and in some cases, improve data legibility and analysis relative to digital alternatives. However, prior empirical studies have only investigated abstract data encoded in physical form (e.g., laser cut bar charts) and not continuously sampled spatial data fields relevant to climate and medical science (e.g., heights, temperatures, densities, and velocities sampled on a spatial grid). This paper presents the design and results of the first study to characterize human performance in 3D spatial data analysis tasks across analogous physical and digital visualizations. Participants analyzed continuous spatial elevation data with three visualization modalities: (1) 2D digital visualization; (2) perspective-tracked, stereoscopic "fishtank" virtual reality; and (3) 3D printed data physicalization. Their tasks included tracing paths downhill, looking up spatial locations and comparing their relative heights, and identifying and reporting the minimum and maximum heights within certain spatial regions. As hypothesized, in most cases, participants performed the tasks just as well or better in the physical modality (based on time and error metrics). Additional results include an analysis of open-ended feedback from participants and discussion of implications for further research on the value of data physicalization. All data and supplemental materials are available at https://osf.io/7xdq4/.

IEEE VIS 2024 Content: Touching the Ground: Evaluating the Effectiveness of Data Physicalizations for Spatial Data Analysis Tasks

Honorable Mention

Touching the Ground: Evaluating the Effectiveness of Data Physicalizations for Spatial Data Analysis Tasks

Bridger Herman - University of Minnesota, Minneapolis, United States

Cullen D. Jackson - Beth Israel Deaconess Medical Center, Boston, United States

Daniel F. Keefe - University of Minnesota, Minneapolis, United States

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Room: Bayshore I

2024-10-17T18:21:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:21:00Z
Exemplar figure, described by caption below
Data physicalizations provide many potential benefits over digital data displays, including haptic perception and body-centric judgments. This paper compares the effectiveness of physicalizations (left) with virtual reality (right top) and 2D visualizations (right bottom) for spatial data analysis tasks on digital elevation data common in climate science and natural resource management.
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Keywords

Data physicalization, virtual reality, evaluation.

Abstract

Abstract—Inspired by recent advances in digital fabrication, artists and scientists have demonstrated that physical data encodings (i.e., data physicalizations) can increase engagement with data, foster collaboration, and in some cases, improve data legibility and analysis relative to digital alternatives. However, prior empirical studies have only investigated abstract data encoded in physical form (e.g., laser cut bar charts) and not continuously sampled spatial data fields relevant to climate and medical science (e.g., heights, temperatures, densities, and velocities sampled on a spatial grid). This paper presents the design and results of the first study to characterize human performance in 3D spatial data analysis tasks across analogous physical and digital visualizations. Participants analyzed continuous spatial elevation data with three visualization modalities: (1) 2D digital visualization; (2) perspective-tracked, stereoscopic "fishtank" virtual reality; and (3) 3D printed data physicalization. Their tasks included tracing paths downhill, looking up spatial locations and comparing their relative heights, and identifying and reporting the minimum and maximum heights within certain spatial regions. As hypothesized, in most cases, participants performed the tasks just as well or better in the physical modality (based on time and error metrics). Additional results include an analysis of open-ended feedback from participants and discussion of implications for further research on the value of data physicalization. All data and supplemental materials are available at https://osf.io/7xdq4/.

IEEE VIS 2024 Content: "It's a Good Idea to Put It Into Words": Writing 'Rudders' in the Initial Stages of Visualization Design

"It's a Good Idea to Put It Into Words": Writing 'Rudders' in the Initial Stages of Visualization Design

Chase Stokes - UC Berkeley, Berkeley, United States

Clara Hu - Self, Berkeley, United States

Marti Hearst - UC Berkeley, Berkeley, United States

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Room: Bayshore II

2024-10-17T16:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:00:00Z
Exemplar figure, described by caption below
Main findings from two interview studies. Right: number of participants who currently use writing in visualization design, and with what frequency, in each design step. Both Study 1 and Study 2 found that visualization designers rarely use writing as a concrete design step. Left: Four types of writing rudders tested in Study 2, participants ratings of each type, and examples of participant-written rudders.
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Keywords

Visualization, design, language, text

Abstract

Written language is a useful tool for non-visual creative activities like composing essays and planning searches. This paper investigates the integration of written language into the visualization design process. We create the idea of a 'writing rudder,' which acts as a guiding force or strategy for the designer. Via an interview study of 24 working visualization designers, we first established that only a minority of participants systematically use writingto aid in design. A second study with 15 visualization designers examined four different variants of written rudders: asking questions, stating conclusions, composing a narrative, and writing titles. Overall, participants had a positive reaction; designers recognized the benefits of explicitly writing down components of the design and indicated that they would use this approach in future design work.More specifically, two approaches - writing questions and writing conclusions/takeaways - were seen as beneficial across the design process, while writing narratives showed promise mainly for the creation stage. Although concerns around potential bias during data exploration were raised, participants also discussed strategies to mitigate such concerns. This paper contributes to a deeper understanding of the interplay between language and visualization, and proposes a straightforward, lightweight addition to the visualization design process.

IEEE VIS 2024 Content: "It's a Good Idea to Put It Into Words": Writing 'Rudders' in the Initial Stages of Visualization Design

"It's a Good Idea to Put It Into Words": Writing 'Rudders' in the Initial Stages of Visualization Design

Chase Stokes - UC Berkeley, Berkeley, United States

Clara Hu - Self, Berkeley, United States

Marti Hearst - UC Berkeley, Berkeley, United States

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Room: Bayshore II

2024-10-17T16:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:00:00Z
Exemplar figure, described by caption below
Main findings from two interview studies. Right: number of participants who currently use writing in visualization design, and with what frequency, in each design step. Both Study 1 and Study 2 found that visualization designers rarely use writing as a concrete design step. Left: Four types of writing rudders tested in Study 2, participants ratings of each type, and examples of participant-written rudders.
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Keywords

Visualization, design, language, text

Abstract

Written language is a useful tool for non-visual creative activities like composing essays and planning searches. This paper investigates the integration of written language into the visualization design process. We create the idea of a 'writing rudder,' which acts as a guiding force or strategy for the designer. Via an interview study of 24 working visualization designers, we first established that only a minority of participants systematically use writingto aid in design. A second study with 15 visualization designers examined four different variants of written rudders: asking questions, stating conclusions, composing a narrative, and writing titles. Overall, participants had a positive reaction; designers recognized the benefits of explicitly writing down components of the design and indicated that they would use this approach in future design work.More specifically, two approaches - writing questions and writing conclusions/takeaways - were seen as beneficial across the design process, while writing narratives showed promise mainly for the creation stage. Although concerns around potential bias during data exploration were raised, participants also discussed strategies to mitigate such concerns. This paper contributes to a deeper understanding of the interplay between language and visualization, and proposes a straightforward, lightweight addition to the visualization design process.

IEEE VIS 2024 Content: Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments

Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments

Angie Boggust - Massachusetts Institute of Technology, Cambridge, United States

Venkatesh Sivaraman - Carnegie Mellon University, Pittsburgh, United States

Yannick Assogba - Apple, Cambridge, United States

Donghao Ren - Apple, Seattle, United States

Dominik Moritz - Apple, Pittsburgh, United States

Fred Hohman - Apple, Seattle, United States

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Room: Bayshore V

2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:18:00Z
Exemplar figure, described by caption below
Compress and Compare helps ML practitioners analyze and compare compression experiments. The Model Map helps practitioners understand what experiments were run and find high-performing sequences of operations, while the Model Scatterplot and Selection Details views help compare accuracy and efficiency metrics quantitatively. Our paper describes the challenges that Compress and Compare addresses, how we designed the system, and a study with eight experts demonstrating its potential to support compression workflows.
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Keywords

Efficient machine learning, model compression, visual analytics, model comparison

Abstract

To deploy machine learning models on-device, practitioners use compression algorithms to shrink and speed up models while maintaining their high-quality output. A critical aspect of compression in practice is model comparison, including tracking many compression experiments, identifying subtle changes in model behavior, and negotiating complex accuracy-efficiency trade-offs. However, existing compression tools poorly support comparison, leading to tedious and, sometimes, incomplete analyses spread across disjoint tools. To support real-world comparative workflows, we develop an interactive visual system called Compress and Compare. Within a single interface, Compress and Compare surfaces promising compression strategies by visualizing provenance relationships between compressed models and reveals compression-induced behavior changes by comparing models’ predictions, weights, and activations. We demonstrate how Compress and Compare supports common compression analysis tasks through two case studies, debugging failed compression on generative language models and identifying compression artifacts in image classification models. We further evaluate Compress and Compare in a user study with eight compression experts, illustrating its potential to provide structure to compression workflows, help practitioners build intuition about compression, and encourage thorough analysis of compression’s effect on model behavior. Through these evaluations, we identify compression-specific challenges that future visual analytics tools should consider and Compress and Compare visualizations that may generalize to broader model comparison tasks.

IEEE VIS 2024 Content: Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments

Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments

Angie Boggust - Massachusetts Institute of Technology, Cambridge, United States

Venkatesh Sivaraman - Carnegie Mellon University, Pittsburgh, United States

Yannick Assogba - Apple, Cambridge, United States

Donghao Ren - Apple, Seattle, United States

Dominik Moritz - Apple, Pittsburgh, United States

Fred Hohman - Apple, Seattle, United States

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Room: Bayshore V

2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:18:00Z
Exemplar figure, described by caption below
Compress and Compare helps ML practitioners analyze and compare compression experiments. The Model Map helps practitioners understand what experiments were run and find high-performing sequences of operations, while the Model Scatterplot and Selection Details views help compare accuracy and efficiency metrics quantitatively. Our paper describes the challenges that Compress and Compare addresses, how we designed the system, and a study with eight experts demonstrating its potential to support compression workflows.
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Keywords

Efficient machine learning, model compression, visual analytics, model comparison

Abstract

To deploy machine learning models on-device, practitioners use compression algorithms to shrink and speed up models while maintaining their high-quality output. A critical aspect of compression in practice is model comparison, including tracking many compression experiments, identifying subtle changes in model behavior, and negotiating complex accuracy-efficiency trade-offs. However, existing compression tools poorly support comparison, leading to tedious and, sometimes, incomplete analyses spread across disjoint tools. To support real-world comparative workflows, we develop an interactive visual system called Compress and Compare. Within a single interface, Compress and Compare surfaces promising compression strategies by visualizing provenance relationships between compressed models and reveals compression-induced behavior changes by comparing models’ predictions, weights, and activations. We demonstrate how Compress and Compare supports common compression analysis tasks through two case studies, debugging failed compression on generative language models and identifying compression artifacts in image classification models. We further evaluate Compress and Compare in a user study with eight compression experts, illustrating its potential to provide structure to compression workflows, help practitioners build intuition about compression, and encourage thorough analysis of compression’s effect on model behavior. Through these evaluations, we identify compression-specific challenges that future visual analytics tools should consider and Compress and Compare visualizations that may generalize to broader model comparison tasks.

IEEE VIS 2024 Content: An Empirical Evaluation of the GPT-4 Multimodal Language Model on Visualization Literacy Tasks

An Empirical Evaluation of the GPT-4 Multimodal Language Model on Visualization Literacy Tasks

Alexander Bendeck - Georgia Institute of Technology, Atlanta, United States

John Stasko - Georgia Institute of Technology, Atlanta, United States

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Room: Bayshore I + II + III

2024-10-18T13:18:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:18:00Z
Exemplar figure, described by caption below
Large vision-language models like GPT-4V are extremely powerful, but we have little understanding of their visualization literacy capabilities. We conduct an empirical evaluation of the GPT-4V model on four tasks from the visualization literature related to visualization literacy: (1) the Visualization Literacy Assessment Test (VLAT); (2) a chart question answering dataset; (3) a set of questions about deceptive visualization design choices; and (4) a set of questions about visualizations with misaligned titles. We also release all materials and code to support future research.
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Keywords

Visualization Literacy, Large Language Models, Natural Language

Abstract

Large Language Models (LLMs) like GPT-4 which support multimodal input (i.e., prompts containing images in addition to text) have immense potential to advance visualization research. However, many questions exist about the visual capabilities of such models, including how well they can read and interpret visually represented data. In our work, we address this question by evaluating the GPT-4 multimodal LLM using a suite of task sets meant to assess the model's visualization literacy. The task sets are based on existing work in the visualization community addressing both automated chart question answering and human visualization literacy across multiple settings. Our assessment finds that GPT-4 can perform tasks such as recognizing trends and extreme values, and also demonstrates some understanding of visualization design best-practices. By contrast, GPT-4 struggles with simple value retrieval when not provided with the original dataset, lacks the ability to reliably distinguish between colors in charts, and occasionally suffers from hallucination and inconsistency. We conclude by reflecting on the model's strengths and weaknesses as well as the potential utility of models like GPT-4 for future visualization research. We also release all code, stimuli, and results for the task sets at the following link: https://doi.org/10.17605/OSF.IO/F39J6

IEEE VIS 2024 Content: An Empirical Evaluation of the GPT-4 Multimodal Language Model on Visualization Literacy Tasks

An Empirical Evaluation of the GPT-4 Multimodal Language Model on Visualization Literacy Tasks

Alexander Bendeck - Georgia Institute of Technology, Atlanta, United States

John Stasko - Georgia Institute of Technology, Atlanta, United States

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Room: Bayshore I + II + III

2024-10-18T13:18:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:18:00Z
Exemplar figure, described by caption below
Large vision-language models like GPT-4V are extremely powerful, but we have little understanding of their visualization literacy capabilities. We conduct an empirical evaluation of the GPT-4V model on four tasks from the visualization literature related to visualization literacy: (1) the Visualization Literacy Assessment Test (VLAT); (2) a chart question answering dataset; (3) a set of questions about deceptive visualization design choices; and (4) a set of questions about visualizations with misaligned titles. We also release all materials and code to support future research.
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Keywords

Visualization Literacy, Large Language Models, Natural Language

Abstract

Large Language Models (LLMs) like GPT-4 which support multimodal input (i.e., prompts containing images in addition to text) have immense potential to advance visualization research. However, many questions exist about the visual capabilities of such models, including how well they can read and interpret visually represented data. In our work, we address this question by evaluating the GPT-4 multimodal LLM using a suite of task sets meant to assess the model's visualization literacy. The task sets are based on existing work in the visualization community addressing both automated chart question answering and human visualization literacy across multiple settings. Our assessment finds that GPT-4 can perform tasks such as recognizing trends and extreme values, and also demonstrates some understanding of visualization design best-practices. By contrast, GPT-4 struggles with simple value retrieval when not provided with the original dataset, lacks the ability to reliably distinguish between colors in charts, and occasionally suffers from hallucination and inconsistency. We conclude by reflecting on the model's strengths and weaknesses as well as the potential utility of models like GPT-4 for future visualization research. We also release all code, stimuli, and results for the task sets at the following link: https://doi.org/10.17605/OSF.IO/F39J6

IEEE VIS 2024 Content: CompositingVis: Exploring Interaction for Creating Composite Visualizations in Immersive Environments

CompositingVis: Exploring Interaction for Creating Composite Visualizations in Immersive Environments

Qian Zhu - The Hong Kong University of Science and Technology, Hong Kong, China. The Hong Kong University of Science and Technology, Hong Kong, China

Tao Lu - Georgia Institute of Technology, Atlanta, United States. Georgia Institute of Technology, Atlanta, United States

Shunan Guo - Adobe Research, San Jose, United States. Adobe Research, San Jose, United States

Xiaojuan Ma - Hong Kong University of Science and Technology, Hong Kong, Hong Kong. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Yalong Yang - Georgia Institute of Technology, Atlanta, United States. Georgia Institute of Technology, Atlanta, United States

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Room: Bayshore II

2024-10-16T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:30:00Z
Exemplar figure, described by caption below
This image shows the five cases that represent the idea of our paper: Using embodied interaction to create composite visualization in immersive environments.
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Keywords

Composite Visualization, Immersive Analytics, Embodied Interaction

Abstract

Composite visualization represents a widely embraced design that combines multiple visual representations to create an integrated view.However, the traditional approach of creating composite visualizations in immersive environments typically occurs asynchronously outside of the immersive space and is carried out by experienced experts.In this work, we aim to empower users to participate in the creation of composite visualization within immersive environments through embodied interactions.This could provide a flexible and fluid experience with immersive visualization and has the potential to facilitate understanding of the relationship between visualization views. We begin with developing a design space of embodied interactions to create various types of composite visualizations with the consideration of data relationships. Drawing inspiration from people's natural experience of manipulating physical objects, we design interactions based on the combination of 3D manipulations in immersive environments. Building upon the design space, we present a series of case studies showcasing the interaction to create different kinds of composite visualizations in virtual reality.Subsequently, we conduct a user study to evaluate the usability of the derived interaction techniques and user experience of creating composite visualizations through embodied interactions.We find that empowering users to participate in composite visualizations through embodied interactions enables them to flexibly leverage different visualization views for understanding and communicating the relationships between different views, which underscores the potential of several future application scenarios.

IEEE VIS 2024 Content: CompositingVis: Exploring Interaction for Creating Composite Visualizations in Immersive Environments

CompositingVis: Exploring Interaction for Creating Composite Visualizations in Immersive Environments

Qian Zhu - The Hong Kong University of Science and Technology, Hong Kong, China. The Hong Kong University of Science and Technology, Hong Kong, China

Tao Lu - Georgia Institute of Technology, Atlanta, United States. Georgia Institute of Technology, Atlanta, United States

Shunan Guo - Adobe Research, San Jose, United States. Adobe Research, San Jose, United States

Xiaojuan Ma - Hong Kong University of Science and Technology, Hong Kong, Hong Kong. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Yalong Yang - Georgia Institute of Technology, Atlanta, United States. Georgia Institute of Technology, Atlanta, United States

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Room: Bayshore II

2024-10-16T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:30:00Z
Exemplar figure, described by caption below
This image shows the five cases that represent the idea of our paper: Using embodied interaction to create composite visualization in immersive environments.
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Keywords

Composite Visualization, Immersive Analytics, Embodied Interaction

Abstract

Composite visualization represents a widely embraced design that combines multiple visual representations to create an integrated view.However, the traditional approach of creating composite visualizations in immersive environments typically occurs asynchronously outside of the immersive space and is carried out by experienced experts.In this work, we aim to empower users to participate in the creation of composite visualization within immersive environments through embodied interactions.This could provide a flexible and fluid experience with immersive visualization and has the potential to facilitate understanding of the relationship between visualization views. We begin with developing a design space of embodied interactions to create various types of composite visualizations with the consideration of data relationships. Drawing inspiration from people's natural experience of manipulating physical objects, we design interactions based on the combination of 3D manipulations in immersive environments. Building upon the design space, we present a series of case studies showcasing the interaction to create different kinds of composite visualizations in virtual reality.Subsequently, we conduct a user study to evaluate the usability of the derived interaction techniques and user experience of creating composite visualizations through embodied interactions.We find that empowering users to participate in composite visualizations through embodied interactions enables them to flexibly leverage different visualization views for understanding and communicating the relationships between different views, which underscores the potential of several future application scenarios.

IEEE VIS 2024 Content: SimpleSets: Capturing Categorical Point Patterns with Simple Shapes

SimpleSets: Capturing Categorical Point Patterns with Simple Shapes

Steven van den Broek - TU Eindhoven, Eindhoven, Netherlands

Wouter Meulemans - TU Eindhoven, Eindhoven, Netherlands

Bettina Speckmann - TU Eindhoven, Eindhoven, Netherlands

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Room: Bayshore VII

2024-10-16T15:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T15:15:00Z
Exemplar figure, described by caption below
A SimpleSets visualization of mills around Leeuwarden, The Netherlands. The mill types are: angular mill (blue); vertical wind engine (green); spider head mill (orange); and tjasker (purple). Data by https://molendatabase.nl with permission, map from https://www.openstreetmap.org/copyright.
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Keywords

Set visualization, geographic visualization, algorithms

Abstract

Points of interest on a map such as restaurants, hotels, or subway stations, give rise to categorical point data: data that have a fixed location and one or more categorical attributes. Consequently, recent years have seen various set visualization approaches that visually connect points of the same category to support users in understanding the spatial distribution of categories. Existing methods use complex and often highly irregular shapes to connect points of the same category, leading to high cognitive load for the user. In this paper we introduce SimpleSets, which uses simple shapes to enclose categorical point patterns, thereby providing a clean overview of the data distribution. SimpleSets is designed to visualize sets of points with a single categorical attribute; as a result, the point patterns enclosed by SimpleSets form a partition of the data. We give formal definitions of point patterns that correspond to simple shapes and describe an algorithm that partitions categorical points into few such patterns. Our second contribution is a rendering algorithm that transforms a given partition into a clean set of shapes resulting in an aesthetically pleasing set visualization. Our algorithm pays particular attention to resolving intersections between nearby shapes in a consistent manner. We compare SimpleSets to the state-of-the-art set visualizations using standard datasets from the literature.

IEEE VIS 2024 Content: SimpleSets: Capturing Categorical Point Patterns with Simple Shapes

SimpleSets: Capturing Categorical Point Patterns with Simple Shapes

Steven van den Broek - TU Eindhoven, Eindhoven, Netherlands

Wouter Meulemans - TU Eindhoven, Eindhoven, Netherlands

Bettina Speckmann - TU Eindhoven, Eindhoven, Netherlands

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Room: Bayshore VII

2024-10-16T15:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T15:15:00Z
Exemplar figure, described by caption below
A SimpleSets visualization of mills around Leeuwarden, The Netherlands. The mill types are: angular mill (blue); vertical wind engine (green); spider head mill (orange); and tjasker (purple). Data by https://molendatabase.nl with permission, map from https://www.openstreetmap.org/copyright.
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Keywords

Set visualization, geographic visualization, algorithms

Abstract

Points of interest on a map such as restaurants, hotels, or subway stations, give rise to categorical point data: data that have a fixed location and one or more categorical attributes. Consequently, recent years have seen various set visualization approaches that visually connect points of the same category to support users in understanding the spatial distribution of categories. Existing methods use complex and often highly irregular shapes to connect points of the same category, leading to high cognitive load for the user. In this paper we introduce SimpleSets, which uses simple shapes to enclose categorical point patterns, thereby providing a clean overview of the data distribution. SimpleSets is designed to visualize sets of points with a single categorical attribute; as a result, the point patterns enclosed by SimpleSets form a partition of the data. We give formal definitions of point patterns that correspond to simple shapes and describe an algorithm that partitions categorical points into few such patterns. Our second contribution is a rendering algorithm that transforms a given partition into a clean set of shapes resulting in an aesthetically pleasing set visualization. Our algorithm pays particular attention to resolving intersections between nearby shapes in a consistent manner. We compare SimpleSets to the state-of-the-art set visualizations using standard datasets from the literature.

IEEE VIS 2024 Content: Charting EDA: How Visualizations and Interactions Shape Analysis in Computational Notebooks.

Charting EDA: How Visualizations and Interactions Shape Analysis in Computational Notebooks.

Dylan Wootton - MIT, Cambridge, United States

Amy Rae Fox - MIT, Cambridge, United States

Evan Peck - University of Colorado Boulder, Boulder, United States

Arvind Satyanarayan - MIT, Cambridge, United States

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Room: Bayshore V

2024-10-16T17:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:45:00Z
Exemplar figure, described by caption below
A diagram illustrating a mixed-methods study of Exploratory Data Analysis (EDA) practices. The left section shows 13 data scientists conducting two EDAs, first with static charts, then with static and interactive charts. Think-aloud utterances and interaction traces are collected from these sessions. The middle section depicts how this data is processed: utterances are coded via content analysis to create observations, which are combined with interaction data to form a comprehensive dataset of EDA sessions. EDA metrics such as revisit rate and hover time are computed from this dataset. The right section demonstrates a formal description of EDA sessions, showing examples of how participants' actions and observations are encoded, including creating visualizations, commenting on distributions, and identifying relationships using various chart types. This systematic approach combines qualitative data collection with quantitative analysis to provide insights into EDA behaviors and strategies.
Keywords

Interaction Design, Methodologies, HumanQual, HumanQuant.

Abstract

Interactive visualizations are powerful tools for Exploratory Data Analysis (EDA), but how do they affect the observations analysts make about their data? We conducted a qualitative experiment with 13 professional data scientists analyzing two datasets with Jupyter notebooks, collecting a rich dataset of interaction traces and think-aloud utterances. By qualitatively coding participant utterances, we introduce a formalism that describes EDA as a sequence of analysis states, where each state is comprised of either a representation an analyst constructs (e.g., the output of a data frame, an interactive visualization, etc.) or an observation the analyst makes (e.g., about missing data, the relationship between variables, etc.). By applying our formalism to our dataset, we identify that interactive visualizations, on average, lead to earlier and more complex insights about relationships between dataset attributes compared to static visualizations. Moreover, by calculating metrics such as revisit count and representational diversity, we uncover that some representations serve more as "planning aids" during EDA rather than tools strictly for hypothesis-answering. We show how these measures help identify other patterns of analysis behavior, such as the "80-20 rule", where a small subset of representations drove the majority of observations. Based on these findings, we offer design guidelines for interactive exploratory analysis tooling and reflect on future directions for studying the role that visualizations play in EDA.

IEEE VIS 2024 Content: Charting EDA: How Visualizations and Interactions Shape Analysis in Computational Notebooks.

Charting EDA: How Visualizations and Interactions Shape Analysis in Computational Notebooks.

Dylan Wootton - MIT, Cambridge, United States

Amy Rae Fox - MIT, Cambridge, United States

Evan Peck - University of Colorado Boulder, Boulder, United States

Arvind Satyanarayan - MIT, Cambridge, United States

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Room: Bayshore V

2024-10-16T17:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:45:00Z
Exemplar figure, described by caption below
A diagram illustrating a mixed-methods study of Exploratory Data Analysis (EDA) practices. The left section shows 13 data scientists conducting two EDAs, first with static charts, then with static and interactive charts. Think-aloud utterances and interaction traces are collected from these sessions. The middle section depicts how this data is processed: utterances are coded via content analysis to create observations, which are combined with interaction data to form a comprehensive dataset of EDA sessions. EDA metrics such as revisit rate and hover time are computed from this dataset. The right section demonstrates a formal description of EDA sessions, showing examples of how participants' actions and observations are encoded, including creating visualizations, commenting on distributions, and identifying relationships using various chart types. This systematic approach combines qualitative data collection with quantitative analysis to provide insights into EDA behaviors and strategies.
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Keywords

Interaction Design, Methodologies, HumanQual, HumanQuant.

Abstract

Interactive visualizations are powerful tools for Exploratory Data Analysis (EDA), but how do they affect the observations analysts make about their data? We conducted a qualitative experiment with 13 professional data scientists analyzing two datasets with Jupyter notebooks, collecting a rich dataset of interaction traces and think-aloud utterances. By qualitatively coding participant utterances, we introduce a formalism that describes EDA as a sequence of analysis states, where each state is comprised of either a representation an analyst constructs (e.g., the output of a data frame, an interactive visualization, etc.) or an observation the analyst makes (e.g., about missing data, the relationship between variables, etc.). By applying our formalism to our dataset, we identify that interactive visualizations, on average, lead to earlier and more complex insights about relationships between dataset attributes compared to static visualizations. Moreover, by calculating metrics such as revisit count and representational diversity, we uncover that some representations serve more as "planning aids" during EDA rather than tools strictly for hypothesis-answering. We show how these measures help identify other patterns of analysis behavior, such as the "80-20 rule", where a small subset of representations drove the majority of observations. Based on these findings, we offer design guidelines for interactive exploratory analysis tooling and reflect on future directions for studying the role that visualizations play in EDA.

IEEE VIS 2024 Content: ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics

ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics

Zherui Zhang - Southern University of Science and Technology, Shenzhen, China

Fan Yang - Southern University of Science and Technology, Shenzhen, China

Ran Cheng - Southern University of Science and Technology, Shenzhen, China

Yuxin Ma - Southern University of Science and Technology, Shenzhen, China

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Room: Bayshore V

2024-10-17T13:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:30:00Z
Exemplar figure, described by caption below
We introduce ParetoTracker, a visual analytics framework designed to illustrate the dynamics of population generations within evolutionary processes of MOEAs, which consists of three main components: Performance Overview and Generation Statistics (A) Visual Exploration of Individuals among Generations (B) In-depth Visual Inspection of Operators (C).
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Keywords

Visual analytics, multi-objective evolutionary algorithms, evolutionary computation

Abstract

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful tools for solving complex optimization problems characterized by multiple, often conflicting, objectives. While advancements have been made in computational efficiency as well as diversity and convergence of solutions, a critical challenge persists: the internal evolutionary mechanisms are opaque to human users. Drawing upon the successes of explainable AI in explaining complex algorithms and models, we argue that the need to understand the underlying evolutionary operators and population dynamics within MOEAs aligns well with a visual analytics paradigm. This paper introduces ParetoTracker, a visual analytics framework designed to support the comprehension and inspection of population dynamics in the evolutionary processes of MOEAs. Informed by preliminary literature review and expert interviews, the framework establishes a multi-level analysis scheme, which caters to user engagement and exploration ranging from examining overall trends in performance metrics to conducting fine-grained inspections of evolutionary operations. In contrast to conventional practices that require manual plotting of solutions for each generation, ParetoTracker facilitates the examination of temporal trends and dynamics across consecutive generations in an integrated visual interface. The effectiveness of the framework is demonstrated through case studies and expert interviews focused on widely adopted benchmark optimization problems.

IEEE VIS 2024 Content: ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics

ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics

Zherui Zhang - Southern University of Science and Technology, Shenzhen, China

Fan Yang - Southern University of Science and Technology, Shenzhen, China

Ran Cheng - Southern University of Science and Technology, Shenzhen, China

Yuxin Ma - Southern University of Science and Technology, Shenzhen, China

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Room: Bayshore V

2024-10-17T13:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:30:00Z
Exemplar figure, described by caption below
We introduce ParetoTracker, a visual analytics framework designed to illustrate the dynamics of population generations within evolutionary processes of MOEAs, which consists of three main components: Performance Overview and Generation Statistics (A) Visual Exploration of Individuals among Generations (B) In-depth Visual Inspection of Operators (C).
Fast forward
Keywords

Visual analytics, multi-objective evolutionary algorithms, evolutionary computation

Abstract

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful tools for solving complex optimization problems characterized by multiple, often conflicting, objectives. While advancements have been made in computational efficiency as well as diversity and convergence of solutions, a critical challenge persists: the internal evolutionary mechanisms are opaque to human users. Drawing upon the successes of explainable AI in explaining complex algorithms and models, we argue that the need to understand the underlying evolutionary operators and population dynamics within MOEAs aligns well with a visual analytics paradigm. This paper introduces ParetoTracker, a visual analytics framework designed to support the comprehension and inspection of population dynamics in the evolutionary processes of MOEAs. Informed by preliminary literature review and expert interviews, the framework establishes a multi-level analysis scheme, which caters to user engagement and exploration ranging from examining overall trends in performance metrics to conducting fine-grained inspections of evolutionary operations. In contrast to conventional practices that require manual plotting of solutions for each generation, ParetoTracker facilitates the examination of temporal trends and dynamics across consecutive generations in an integrated visual interface. The effectiveness of the framework is demonstrated through case studies and expert interviews focused on widely adopted benchmark optimization problems.

IEEE VIS 2024 Content: Does This Have a Particular Meaning?: Interactive Pattern Explanation for Network Visualizations

Does This Have a Particular Meaning?: Interactive Pattern Explanation for Network Visualizations

Xinhuan Shu - Newcastle University, Newcastle Upon Tyne, United Kingdom. University of Edinburgh, Edinburgh, United Kingdom

Alexis Pister - University of Edinburgh, Edinburgh, United Kingdom

Junxiu Tang - Zhejiang University, Hangzhou, China

Fanny Chevalier - University of Toronto, Toronto, Canada

Benjamin Bach - Inria, Bordeaux, France. University of Edinburgh, Edinburgh, United Kingdom

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Room: Bayshore VII

2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
We propose Pattern Explainer to help analysts who are unfamiliar with network visualizations learn about visual patterns in the representation of their data. Looking at the visualization, a user spots a visual pattern of interest, e.g. a “bug”-looking pattern in the matrix. To inquire about whether this pattern is meaningful, the user selects the area. Pattern Explainer then automatically mines the selection, against a dictionary of network motifs, and provides the user with explanations of what underlying network patterns the visual pattern reveals.
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Keywords

Visualization education, network visualization

Abstract

This paper presents an interactive technique to explain visual patterns in network visualizations to analysts who do not understand these visualizations and who are learning to read them. Learning a visualization requires mastering its visual grammar and decoding information presented through visual marks, graphical encodings, and spatial configurations. To help people learn network visualization designs and extract meaningful information, we introduce the concept of interactive pattern explanation that allows viewers to select an arbitrary area in a visualization, then automatically mines the underlying data patterns, and explains both visual and data patterns present in the viewer’s selection. In a qualitative and a quantitative user study with a total of 32 participants, we compare interactive pattern explanations to textual-only and visual-only (cheatsheets) explanations. Our results show that interactive explanations increase learning of i) unfamiliar visualizations, ii) patterns in network science, and iii) the respective network terminology.

IEEE VIS 2024 Content: Does This Have a Particular Meaning?: Interactive Pattern Explanation for Network Visualizations

Does This Have a Particular Meaning?: Interactive Pattern Explanation for Network Visualizations

Xinhuan Shu - Newcastle University, Newcastle Upon Tyne, United Kingdom. University of Edinburgh, Edinburgh, United Kingdom

Alexis Pister - University of Edinburgh, Edinburgh, United Kingdom

Junxiu Tang - Zhejiang University, Hangzhou, China

Fanny Chevalier - University of Toronto, Toronto, Canada

Benjamin Bach - Inria, Bordeaux, France. University of Edinburgh, Edinburgh, United Kingdom

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Room: Bayshore VII

2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
We propose Pattern Explainer to help analysts who are unfamiliar with network visualizations learn about visual patterns in the representation of their data. Looking at the visualization, a user spots a visual pattern of interest, e.g. a “bug”-looking pattern in the matrix. To inquire about whether this pattern is meaningful, the user selects the area. Pattern Explainer then automatically mines the selection, against a dictionary of network motifs, and provides the user with explanations of what underlying network patterns the visual pattern reveals.
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Keywords

Visualization education, network visualization

Abstract

This paper presents an interactive technique to explain visual patterns in network visualizations to analysts who do not understand these visualizations and who are learning to read them. Learning a visualization requires mastering its visual grammar and decoding information presented through visual marks, graphical encodings, and spatial configurations. To help people learn network visualization designs and extract meaningful information, we introduce the concept of interactive pattern explanation that allows viewers to select an arbitrary area in a visualization, then automatically mines the underlying data patterns, and explains both visual and data patterns present in the viewer’s selection. In a qualitative and a quantitative user study with a total of 32 participants, we compare interactive pattern explanations to textual-only and visual-only (cheatsheets) explanations. Our results show that interactive explanations increase learning of i) unfamiliar visualizations, ii) patterns in network science, and iii) the respective network terminology.

IEEE VIS 2024 Content: Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning

Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning

Xingchen Zeng - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Haichuan Lin - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Yilin Ye - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Wei Zeng - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. The Hong Kong University of Science and Technology, Hong Kong SAR, China

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Room: Bayshore V

2024-10-18T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:06:00Z
Exemplar figure, described by caption below
Comparison of our model with state-of-the-art MLLMs on chart question answering. Existing MLLMs often fail to understand visual mappings, such as inverted Y-axis, truncated axis, bubble sizing, and area stacking. In contrast, our model, trained with the visualization-referenced dataset we constructed, showcases a better understanding of visualization domain knowledge.
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Keywords

Chart-question answering, multimodal large language models, benchmark

Abstract

Emerging multimodal large language models (MLLMs) exhibit great potential for chart question answering (CQA). Recent efforts primarily focus on scaling up training datasets (i.e., charts, data tables, and question-answer (QA) pairs) through data collection and synthesis. However, our empirical study on existing MLLMs and CQA datasets reveals notable gaps. First, current data collection and synthesis focus on data volume and lack consideration of fine-grained visual encodings and QA tasks, resulting in unbalanced data distribution divergent from practical CQA scenarios. Second, existing work follows the training recipe of the base MLLMs initially designed for natural images, under-exploring the adaptation to unique chart characteristics, such as rich text elements. To fill the gap, we propose a visualization-referenced instruction tuning approach to guide the training dataset enhancement and model development. Specifically, we propose a novel data engine to effectively filter diverse and high-quality data from existing datasets and subsequently refine and augment the data using LLM-based generation techniques to better align with practical QA tasks and visual encodings. Then, to facilitate the adaptation to chart characteristics, we utilize the enriched data to train an MLLM by unfreezing the vision encoder and incorporating a mixture-of-resolution adaptation strategy for enhanced fine-grained recognition. Experimental results validate the effectiveness of our approach. Even with fewer training examples, our model consistently outperforms state-of-the-art CQA models on established benchmarks. We also contribute a dataset split as a benchmark for future research. Source codes and datasets of this paper are available at https://github.com/zengxingchen/ChartQA-MLLM.

IEEE VIS 2024 Content: Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning

Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning

Xingchen Zeng - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Haichuan Lin - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Yilin Ye - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Wei Zeng - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. The Hong Kong University of Science and Technology, Hong Kong SAR, China

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Room: Bayshore V

2024-10-18T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:06:00Z
Exemplar figure, described by caption below
Comparison of our model with state-of-the-art MLLMs on chart question answering. Existing MLLMs often fail to understand visual mappings, such as inverted Y-axis, truncated axis, bubble sizing, and area stacking. In contrast, our model, trained with the visualization-referenced dataset we constructed, showcases a better understanding of visualization domain knowledge.
Fast forward
Keywords

Chart-question answering, multimodal large language models, benchmark

Abstract

Emerging multimodal large language models (MLLMs) exhibit great potential for chart question answering (CQA). Recent efforts primarily focus on scaling up training datasets (i.e., charts, data tables, and question-answer (QA) pairs) through data collection and synthesis. However, our empirical study on existing MLLMs and CQA datasets reveals notable gaps. First, current data collection and synthesis focus on data volume and lack consideration of fine-grained visual encodings and QA tasks, resulting in unbalanced data distribution divergent from practical CQA scenarios. Second, existing work follows the training recipe of the base MLLMs initially designed for natural images, under-exploring the adaptation to unique chart characteristics, such as rich text elements. To fill the gap, we propose a visualization-referenced instruction tuning approach to guide the training dataset enhancement and model development. Specifically, we propose a novel data engine to effectively filter diverse and high-quality data from existing datasets and subsequently refine and augment the data using LLM-based generation techniques to better align with practical QA tasks and visual encodings. Then, to facilitate the adaptation to chart characteristics, we utilize the enriched data to train an MLLM by unfreezing the vision encoder and incorporating a mixture-of-resolution adaptation strategy for enhanced fine-grained recognition. Experimental results validate the effectiveness of our approach. Even with fewer training examples, our model consistently outperforms state-of-the-art CQA models on established benchmarks. We also contribute a dataset split as a benchmark for future research. Source codes and datasets of this paper are available at https://github.com/zengxingchen/ChartQA-MLLM.

IEEE VIS 2024 Content: Unmasking Dunning-Kruger Effect in Visual Reasoning and Visual Data Analysis

Unmasking Dunning-Kruger Effect in Visual Reasoning and Visual Data Analysis

Mengyu Chen - Emory University, Atlanta, United States

Yijun Liu - Emory University, Atlanta, United States

Emily Wall - Emory University, Atlanta, United States

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Room: Bayshore II

2024-10-16T14:27:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:27:00Z
Exemplar figure, described by caption below
We replicated the Dunning-Kruger Effect (DKE) across tasks involving visual reasoning and judgment. We observed a typical DKE pattern, where highly skilled people tend to underestimate their performance, while those with lower skills often overestimate it. Additionally, we explored potential indicators of DKE, including participants’ interactions, personality traits, and domain familiarity, and identified several factors related to DKE.
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Keywords

Cognitive Bias, Dunning Kruger Effect, Metacognition, Personality Traits, Interactions, Visual Reasoning

Abstract

The Dunning-Kruger Effect (DKE) is a metacognitive phenomenon where low-skilled individuals tend to overestimate their competence while high-skilled individuals tend to underestimate their competence. This effect has been observed in a number of domains including humor, grammar, and logic. In this paper, we explore if and how DKE manifests in visual reasoning and judgment tasks. Across two online user studies involving (1) a sliding puzzle game and (2) a scatterplot-based categorization task, we demonstrate that individuals are susceptible to DKE in visual reasoning and judgment tasks: those who performed best underestimated their performance, while bottom performers overestimated their performance. In addition, we contribute novel analyses that correlate susceptibility of DKE with personality traits and user interactions. Our findings pave the way for novel modes of bias detection via interaction patterns and establish promising directions towards interventions tailored to an individual’s personality traits. All materials and analyses are in supplemental materials: https://github.com/CAV-Lab/DKE_supplemental.git.

IEEE VIS 2024 Content: Unmasking Dunning-Kruger Effect in Visual Reasoning and Visual Data Analysis

Unmasking Dunning-Kruger Effect in Visual Reasoning and Visual Data Analysis

Mengyu Chen - Emory University, Atlanta, United States

Yijun Liu - Emory University, Atlanta, United States

Emily Wall - Emory University, Atlanta, United States

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Room: Bayshore II

2024-10-16T14:27:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:27:00Z
Exemplar figure, described by caption below
We replicated the Dunning-Kruger Effect (DKE) across tasks involving visual reasoning and judgment. We observed a typical DKE pattern, where highly skilled people tend to underestimate their performance, while those with lower skills often overestimate it. Additionally, we explored potential indicators of DKE, including participants’ interactions, personality traits, and domain familiarity, and identified several factors related to DKE.
Fast forward
Keywords

Cognitive Bias, Dunning Kruger Effect, Metacognition, Personality Traits, Interactions, Visual Reasoning

Abstract

The Dunning-Kruger Effect (DKE) is a metacognitive phenomenon where low-skilled individuals tend to overestimate their competence while high-skilled individuals tend to underestimate their competence. This effect has been observed in a number of domains including humor, grammar, and logic. In this paper, we explore if and how DKE manifests in visual reasoning and judgment tasks. Across two online user studies involving (1) a sliding puzzle game and (2) a scatterplot-based categorization task, we demonstrate that individuals are susceptible to DKE in visual reasoning and judgment tasks: those who performed best underestimated their performance, while bottom performers overestimated their performance. In addition, we contribute novel analyses that correlate susceptibility of DKE with personality traits and user interactions. Our findings pave the way for novel modes of bias detection via interaction patterns and establish promising directions towards interventions tailored to an individual’s personality traits. All materials and analyses are in supplemental materials: https://github.com/CAV-Lab/DKE_supplemental.git.

IEEE VIS 2024 Content: ProvenanceWidgets: A Library of UI Control Elements to Track and Dynamically Overlay Analytic Provenance

ProvenanceWidgets: A Library of UI Control Elements to Track and Dynamically Overlay Analytic Provenance

Arpit Narechania - Georgia Institute of Technology, Atlanta, United States

Kaustubh Odak - Georgia Institute of Technology, Atlanta, United States

Mennatallah El-Assady - ETH Zürich, Zürich, Switzerland

Alex Endert - Georgia Institute of Technology, Atlanta, United States

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Room: Bayshore V

2024-10-16T18:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T18:45:00Z
Exemplar figure, described by caption below
ProvenanceWidgets is a new open-source JavaScript library of UI controls such as range sliders and dropdowns to track and dynamically overlay analytic provenance. Install it as "npm install provenance-widgets".
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Keywords

Provenance, Analytic provenance, Visualization, UI controls, GUI elements, JavaScript library.

Abstract

We present ProvenanceWidgets, a Javascript library of UI control elements such as radio buttons, checkboxes, and dropdowns to track and dynamically overlay a user's analytic provenance. These in situ overlays not only save screen space but also minimize the amount of time and effort needed to access the same information from elsewhere in the UI. In this paper, we discuss how we design modular UI control elements to track how often and how recently a user interacts with them and design visual overlays showing an aggregated summary as well as a detailed temporal history. We demonstrate the capability of ProvenanceWidgets by recreating three prior widget libraries: (1) Scented Widgets, (2) Phosphor objects, and (3) Dynamic Query Widgets. We also evaluated its expressiveness and conducted case studies with visualization developers to evaluate its effectiveness. We find that ProvenanceWidgets enables developers to implement custom provenance-tracking applications effectively. ProvenanceWidgets is available as open-source software at https://github.com/ProvenanceWidgets to help application developers build custom provenance-based systems.

IEEE VIS 2024 Content: ProvenanceWidgets: A Library of UI Control Elements to Track and Dynamically Overlay Analytic Provenance

ProvenanceWidgets: A Library of UI Control Elements to Track and Dynamically Overlay Analytic Provenance

Arpit Narechania - Georgia Institute of Technology, Atlanta, United States

Kaustubh Odak - Georgia Institute of Technology, Atlanta, United States

Mennatallah El-Assady - ETH Zürich, Zürich, Switzerland

Alex Endert - Georgia Institute of Technology, Atlanta, United States

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Room: Bayshore V

2024-10-16T18:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T18:45:00Z
Exemplar figure, described by caption below
ProvenanceWidgets is a new open-source JavaScript library of UI controls such as range sliders and dropdowns to track and dynamically overlay analytic provenance. Install it as "npm install provenance-widgets".
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Keywords

Provenance, Analytic provenance, Visualization, UI controls, GUI elements, JavaScript library.

Abstract

We present ProvenanceWidgets, a Javascript library of UI control elements such as radio buttons, checkboxes, and dropdowns to track and dynamically overlay a user's analytic provenance. These in situ overlays not only save screen space but also minimize the amount of time and effort needed to access the same information from elsewhere in the UI. In this paper, we discuss how we design modular UI control elements to track how often and how recently a user interacts with them and design visual overlays showing an aggregated summary as well as a detailed temporal history. We demonstrate the capability of ProvenanceWidgets by recreating three prior widget libraries: (1) Scented Widgets, (2) Phosphor objects, and (3) Dynamic Query Widgets. We also evaluated its expressiveness and conducted case studies with visualization developers to evaluate its effectiveness. We find that ProvenanceWidgets enables developers to implement custom provenance-tracking applications effectively. ProvenanceWidgets is available as open-source software at https://github.com/ProvenanceWidgets to help application developers build custom provenance-based systems.

IEEE VIS 2024 Content: Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts

Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts

Nora Al-Naami - Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

Nicolas Medoc - Luxembourg Institute of Science and Technology, Belvaux, Luxembourg

Matteo Magnani - Uppsala University, Uppsala, Sweden

Mohammad Ghoniem - Luxembourg Institute of Science and Technology, Belvaux, Luxembourg

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Room: Bayshore I

2024-10-16T17:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:45:00Z
Exemplar figure, described by caption below
A symmetric arc diagram representing a 51-node graph extracted from the co-occurrence network of characters of "Les Misérables", the novel of Victor Hugo. The nodes are ordered according to the crossing reduction algorithm.
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Keywords

network visualization, arc diagrams, radial diagrams, cluster perception, graph seriation

Abstract

Graphs are often used to model relationships between entities. The identification and visualization of clusters in graphs enable insight discovery in many application areas, such as life sciences and social sciences. Force-directed graph layouts promote the visual saliency of clusters, as they bring adjacent nodes closer together, and push non-adjacent nodes apart. At the same time, matrices can effectively show clusters when a suitable row/column ordering is applied, but are less appealing to untrained users not providing an intuitive node-link metaphor. It is thus worth exploring layouts combining the strengths of the node-link metaphor and node ordering. In this work, we study the impact of node ordering on the visual saliency of clusters in orderable node-link diagrams, namely radial diagrams, arc diagrams and symmetric arc diagrams. Through a crowdsourced controlled experiment, we show that users can count clusters consistently more accurately, and to a large extent faster, with orderable node-link diagrams than with three state-of-the art force-directed layout algorithms, i.e., `Linlog', `Backbone' and `sfdp'. The measured advantage is greater in case of low cluster separability and/or low compactness. A free copy of this paper and all supplemental materials are available at https://osf.io/kc3dg/.

IEEE VIS 2024 Content: Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts

Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts

Nora Al-Naami - Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

Nicolas Medoc - Luxembourg Institute of Science and Technology, Belvaux, Luxembourg

Matteo Magnani - Uppsala University, Uppsala, Sweden

Mohammad Ghoniem - Luxembourg Institute of Science and Technology, Belvaux, Luxembourg

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Room: Bayshore I

2024-10-16T17:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:45:00Z
Exemplar figure, described by caption below
A symmetric arc diagram representing a 51-node graph extracted from the co-occurrence network of characters of "Les Misérables", the novel of Victor Hugo. The nodes are ordered according to the crossing reduction algorithm.
Fast forward
Keywords

network visualization, arc diagrams, radial diagrams, cluster perception, graph seriation

Abstract

Graphs are often used to model relationships between entities. The identification and visualization of clusters in graphs enable insight discovery in many application areas, such as life sciences and social sciences. Force-directed graph layouts promote the visual saliency of clusters, as they bring adjacent nodes closer together, and push non-adjacent nodes apart. At the same time, matrices can effectively show clusters when a suitable row/column ordering is applied, but are less appealing to untrained users not providing an intuitive node-link metaphor. It is thus worth exploring layouts combining the strengths of the node-link metaphor and node ordering. In this work, we study the impact of node ordering on the visual saliency of clusters in orderable node-link diagrams, namely radial diagrams, arc diagrams and symmetric arc diagrams. Through a crowdsourced controlled experiment, we show that users can count clusters consistently more accurately, and to a large extent faster, with orderable node-link diagrams than with three state-of-the art force-directed layout algorithms, i.e., `Linlog', `Backbone' and `sfdp'. The measured advantage is greater in case of low cluster separability and/or low compactness. A free copy of this paper and all supplemental materials are available at https://osf.io/kc3dg/.

IEEE VIS 2024 Content: Graph Transformer for Label Placement

Graph Transformer for Label Placement

Jingwei Qu - Southwest University, Beibei, China

Pingshun Zhang - Southwest University, Chongqing, China

Enyu Che - Southwest University, Beibei, China

Yinan Chen - COLLEGE OF COMPUTER AND INFORMATION SCIENCE, SOUTHWEST UNIVERSITY SCHOOL OF SOFTWAREC, Chongqin, China

Haibin Ling - Stony Brook University, New York, United States

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Room: Bayshore II

2024-10-17T15:03:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T15:03:00Z
Exemplar figure, described by caption below
GNN-driven label placement. For a set of labels to be placed in a graphic, Label Placement Graph Transformer (LPGT) predicts the label layout given the graphic and raw label information. First, a complete graph is constructed to capture the relationship between labels. Its node and edge features are generated from the label information and image features. Next, given the graph as input, LPGT iteratively learns the displacements of the nodes by a sequence of GNN modules. The graph is updated by each module and taken as input for the next module.
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Keywords

Label placement, Graph neural network, Transformer

Abstract

Placing text labels is a common way to explain key elements in a given scene. Given a graphic input and original label information, how to place labels to meet both geometric and aesthetic requirements is an open challenging problem. Geometry-wise, traditional rule-driven solutions struggle to capture the complex interactions between labels, let alone consider graphical/appearance content. In terms of aesthetics, training/evaluation data ideally require nontrivial effort and expertise in design, thus resulting in a lack of decent datasets for learning-based methods. To address the above challenges, we formulate the task with a graph representation, where nodes correspond to labels and edges to interactions between labels, and treat label placement as a node position prediction problem. With this novel representation, we design a Label Placement Graph Transformer (LPGT) to predict label positions. Specifically, edge-level attention, conditioned on node representations, is introduced to reveal potential relationships between labels. To integrate graphic/image information, we design a feature aligning strategy that extracts deep features for nodes and edges efficiently. Next, to address the dataset issue, we collect commercial illustrations with professionally designed label layouts from household appliance manuals, and annotate them with useful information to create a novel dataset named the Appliance Manual Illustration Labels (AMIL) dataset. In the thorough evaluation on AMIL, our LPGT solution achieves promising label placement performance compared with popular baselines. Our algorithm is available at https://github.com/JingweiQu/LPGT.

IEEE VIS 2024 Content: Graph Transformer for Label Placement

Graph Transformer for Label Placement

Jingwei Qu - Southwest University, Beibei, China

Pingshun Zhang - Southwest University, Chongqing, China

Enyu Che - Southwest University, Beibei, China

Yinan Chen - COLLEGE OF COMPUTER AND INFORMATION SCIENCE, SOUTHWEST UNIVERSITY SCHOOL OF SOFTWAREC, Chongqin, China

Haibin Ling - Stony Brook University, New York, United States

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Room: Bayshore II

2024-10-17T15:03:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T15:03:00Z
Exemplar figure, described by caption below
GNN-driven label placement. For a set of labels to be placed in a graphic, Label Placement Graph Transformer (LPGT) predicts the label layout given the graphic and raw label information. First, a complete graph is constructed to capture the relationship between labels. Its node and edge features are generated from the label information and image features. Next, given the graph as input, LPGT iteratively learns the displacements of the nodes by a sequence of GNN modules. The graph is updated by each module and taken as input for the next module.
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Keywords

Label placement, Graph neural network, Transformer

Abstract

Placing text labels is a common way to explain key elements in a given scene. Given a graphic input and original label information, how to place labels to meet both geometric and aesthetic requirements is an open challenging problem. Geometry-wise, traditional rule-driven solutions struggle to capture the complex interactions between labels, let alone consider graphical/appearance content. In terms of aesthetics, training/evaluation data ideally require nontrivial effort and expertise in design, thus resulting in a lack of decent datasets for learning-based methods. To address the above challenges, we formulate the task with a graph representation, where nodes correspond to labels and edges to interactions between labels, and treat label placement as a node position prediction problem. With this novel representation, we design a Label Placement Graph Transformer (LPGT) to predict label positions. Specifically, edge-level attention, conditioned on node representations, is introduced to reveal potential relationships between labels. To integrate graphic/image information, we design a feature aligning strategy that extracts deep features for nodes and edges efficiently. Next, to address the dataset issue, we collect commercial illustrations with professionally designed label layouts from household appliance manuals, and annotate them with useful information to create a novel dataset named the Appliance Manual Illustration Labels (AMIL) dataset. In the thorough evaluation on AMIL, our LPGT solution achieves promising label placement performance compared with popular baselines. Our algorithm is available at https://github.com/JingweiQu/LPGT.

IEEE VIS 2024 Content: Aardvark: Composite Visualizations of Trees, Time-Series, and Images

Best Paper Award

Aardvark: Composite Visualizations of Trees, Time-Series, and Images

Devin Lange - University of Utah, Salt Lake City, United States

Robert L Judson-Torres - University of Utah, Salt Lake City, United States

Thomas A Zangle - University of Utah, Salt Lake City, United States

Alexander Lex - University of Utah, Salt Lake City, United States

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Room: Bayshore I + II + III

2024-10-15T16:25:00Z GMT-0600 Change your timezone on the schedule page
2024-10-15T16:25:00Z
Exemplar figure, described by caption below
Live-cell microscopy imaging results in multimodal data composed of trees, time-series, and images. The visualization system Aardvark combines these data modalities into composite visualizations. The tree-first visualization (left) shows the cell relationships as a node-link tree visualization, horizon charts show the time series data and image snippets display alongside the horizon charts. The time-series-first visualization (top right) shows the time-series data as line charts with images and cell relationships superimposed. Finally, the image-first visualization (bottom right) shows the full microscopy image, with cell movement and relationships superimposed.
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Keywords

Visualization, Cell Microscopy, View Composition

Abstract

How do cancer cells grow, divide, proliferate, and die? How do drugs influence these processes? These are difficult questions that we can attempt to answer with a combination of time-series microscopy experiments, classification algorithms, and data visualization.However, collecting this type of data and applying algorithms to segment and track cells and construct lineages of proliferation is error-prone; and identifying the errors can be challenging since it often requires cross-checking multiple data types. Similarly, analyzing and communicating the results necessitates synthesizing different data types into a single narrative. State-of-the-art visualization methods for such data use independent line charts, tree diagrams, and images in separate views. However, this spatial separation requires the viewer of these charts to combine the relevant pieces of data in memory. To simplify this challenging task, we describe design principles for weaving cell images, time-series data, and tree data into a cohesive visualization. Our design principles are based on choosing a primary data type that drives the layout and integrates the other data types into that layout. We then introduce Aardvark, a system that uses these principles to implement novel visualization techniques. Based on Aardvark, we demonstrate the utility of each of these approaches for discovery, communication, and data debugging in a series of case studies.

IEEE VIS 2024 Content: Aardvark: Composite Visualizations of Trees, Time-Series, and Images

Best Paper Award

Aardvark: Composite Visualizations of Trees, Time-Series, and Images

Devin Lange - University of Utah, Salt Lake City, United States

Robert L Judson-Torres - University of Utah, Salt Lake City, United States

Thomas A Zangle - University of Utah, Salt Lake City, United States

Alexander Lex - University of Utah, Salt Lake City, United States

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Room: Bayshore I + II + III

2024-10-15T16:25:00Z GMT-0600 Change your timezone on the schedule page
2024-10-15T16:25:00Z
Exemplar figure, described by caption below
Live-cell microscopy imaging results in multimodal data composed of trees, time-series, and images. The visualization system Aardvark combines these data modalities into composite visualizations. The tree-first visualization (left) shows the cell relationships as a node-link tree visualization, horizon charts show the time series data and image snippets display alongside the horizon charts. The time-series-first visualization (top right) shows the time-series data as line charts with images and cell relationships superimposed. Finally, the image-first visualization (bottom right) shows the full microscopy image, with cell movement and relationships superimposed.
Fast forward
Keywords

Visualization, Cell Microscopy, View Composition

Abstract

How do cancer cells grow, divide, proliferate, and die? How do drugs influence these processes? These are difficult questions that we can attempt to answer with a combination of time-series microscopy experiments, classification algorithms, and data visualization.However, collecting this type of data and applying algorithms to segment and track cells and construct lineages of proliferation is error-prone; and identifying the errors can be challenging since it often requires cross-checking multiple data types. Similarly, analyzing and communicating the results necessitates synthesizing different data types into a single narrative. State-of-the-art visualization methods for such data use independent line charts, tree diagrams, and images in separate views. However, this spatial separation requires the viewer of these charts to combine the relevant pieces of data in memory. To simplify this challenging task, we describe design principles for weaving cell images, time-series data, and tree data into a cohesive visualization. Our design principles are based on choosing a primary data type that drives the layout and integrates the other data types into that layout. We then introduce Aardvark, a system that uses these principles to implement novel visualization techniques. Based on Aardvark, we demonstrate the utility of each of these approaches for discovery, communication, and data debugging in a series of case studies.

IEEE VIS 2024 Content: Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks

Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks

Klaus Eckelt - Johannes Kepler University Linz, Linz, Austria

Kiran Gadhave - University of Utah, Salt Lake City, United States

Alexander Lex - University of Utah, Salt Lake City, United States

Marc Streit - Johannes Kepler University Linz, Linz, Austria

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Room: Bayshore V

2024-10-16T18:09:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T18:09:00Z
Exemplar figure, described by caption below
Loops tracks and visualizes the provenance of computational notebooks. Compact and detailed visualizations of the notebook's history trace the evolution of the notebook over time and highlight differences between versions. Loops visualizes the provenance of code, markdown, tables, visualizations, and images and can explicitly encode their differences.
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Keywords

Comparative visualization, computational notebooks, provenance, data science

Abstract

Exploratory data science is an iterative process of obtaining, cleaning, profiling, analyzing, and interpreting data. This cyclical way of working creates challenges within the linear structure of computational notebooks, leading to issues with code quality, recall, and reproducibility. To remedy this, we present Loops, a set of visual support techniques for iterative and exploratory data analysis in computational notebooks. Loops leverages provenance information to visualize the impact of changes made within a notebook. In visualizations of the notebook provenance, we trace the evolution of the notebook over time and highlight differences between versions. Loops visualizes the provenance of code, markdown, tables, visualizations, and images and their respective differences. Analysts can explore these differences in detail in a separate view. Loops not only makes the analysis process transparent but also supports analysts in their data science work by showing the effects of changes and facilitating comparison of multiple versions. We demonstrate our approach's utility and potential impact in two use cases and feedback from notebook users from various backgrounds. This paper and all supplemental materials are available at https://osf.io/79eyn.

IEEE VIS 2024 Content: Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks

Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks

Klaus Eckelt - Johannes Kepler University Linz, Linz, Austria

Kiran Gadhave - University of Utah, Salt Lake City, United States

Alexander Lex - University of Utah, Salt Lake City, United States

Marc Streit - Johannes Kepler University Linz, Linz, Austria

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Room: Bayshore V

2024-10-16T18:09:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T18:09:00Z
Exemplar figure, described by caption below
Loops tracks and visualizes the provenance of computational notebooks. Compact and detailed visualizations of the notebook's history trace the evolution of the notebook over time and highlight differences between versions. Loops visualizes the provenance of code, markdown, tables, visualizations, and images and can explicitly encode their differences.
Fast forward
Keywords

Comparative visualization, computational notebooks, provenance, data science

Abstract

Exploratory data science is an iterative process of obtaining, cleaning, profiling, analyzing, and interpreting data. This cyclical way of working creates challenges within the linear structure of computational notebooks, leading to issues with code quality, recall, and reproducibility. To remedy this, we present Loops, a set of visual support techniques for iterative and exploratory data analysis in computational notebooks. Loops leverages provenance information to visualize the impact of changes made within a notebook. In visualizations of the notebook provenance, we trace the evolution of the notebook over time and highlight differences between versions. Loops visualizes the provenance of code, markdown, tables, visualizations, and images and their respective differences. Analysts can explore these differences in detail in a separate view. Loops not only makes the analysis process transparent but also supports analysts in their data science work by showing the effects of changes and facilitating comparison of multiple versions. We demonstrate our approach's utility and potential impact in two use cases and feedback from notebook users from various backgrounds. This paper and all supplemental materials are available at https://osf.io/79eyn.

IEEE VIS 2024 Content: Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

Ratanond Koonchanok - Indiana University, Indianapolis, United States

Michael E. Papka - Argonne National Laboratory, Lemont, United States. University of Illinois Chicago, Chicago, United States

Khairi Reda - Indiana University, Indianapolis, United States

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Room: Bayshore II

2024-10-16T14:39:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:39:00Z
Exemplar figure, described by caption below
In this paper, we compare the ability of humans and statistical models to characterize the mean and uncertainty of the data-generating model based on visualized samples. Our results indicate that humans can outperform statistical models when faced with extreme samples.
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Keywords

Visual inference, statistical rationality, human-machine collaboration

Abstract

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals’ accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://osf.io/qmfv6

IEEE VIS 2024 Content: Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

Ratanond Koonchanok - Indiana University, Indianapolis, United States

Michael E. Papka - Argonne National Laboratory, Lemont, United States. University of Illinois Chicago, Chicago, United States

Khairi Reda - Indiana University, Indianapolis, United States

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Room: Bayshore II

2024-10-16T14:39:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:39:00Z
Exemplar figure, described by caption below
In this paper, we compare the ability of humans and statistical models to characterize the mean and uncertainty of the data-generating model based on visualized samples. Our results indicate that humans can outperform statistical models when faced with extreme samples.
Fast forward
Keywords

Visual inference, statistical rationality, human-machine collaboration

Abstract

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals’ accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://osf.io/qmfv6

IEEE VIS 2024 Content: Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis

Honorable Mention

Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis

Arran Zeyu Wang - University of North Carolina-Chapel Hill, Chapel Hill, United States

David Borland - UNC-Chapel Hill, Chapel Hill, United States

David Gotz - University of North Carolina, Chapel Hill, United States

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Room: Bayshore V

2024-10-17T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T12:30:00Z
Exemplar figure, described by caption below
The proposed counterfactual guidance technique is compared with traditional correlation-based guidance through five scenarios. Using the example question "Will coffee drinking cause differences in students' grades?", an analyst might compare data based on coffee consumption and grade distributions. The leftmost column lists the subsets created, and charts illustrate five potential distribution combinations (a-e), suggesting different answers. Symbols at the bottom indicate which methods accurately interpret the data. Counterfactual-based approaches have advantages in two scenarios and perform equally in the other three.
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Keywords

Counterfactual, Guidance, Exploratory visual analysis, Visual causal inference, Correlation

Abstract

Providing effective guidance for users has long been an important and challenging task for efficient exploratory visual analytics, especially when selecting variables for visualization in high-dimensional datasets. Correlation is the most widely applied metric for guidance in statistical and analytical tools, however a reliance on correlation may lead users towards false positives when interpreting causal relations in the data. In this work, inspired by prior insights on the benefits of counterfactual visualization in supporting visual causal inference, we propose a novel, simple, and efficient counterfactual guidance method to enhance causal inference performance in guided exploratory analytics based on insights and concerns gathered from expert interviews. Our technique aims to capitalize on the benefits of counterfactual approaches while reducing their complexity for users. We integrated counterfactual guidance into an exploratory visual analytics system, and using a synthetically generated ground-truth causal dataset, conducted a comparative user study and evaluated to what extent counterfactual guidance can help lead users to more precise visual causal inferences. The results suggest that counterfactual guidance improved visual causal inference performance, and also led to different exploratory behaviors compared to correlation-based guidance. Based on these findings, we offer future directions and challenges for incorporating counterfactual guidance to better support exploratory visual analytics.

IEEE VIS 2024 Content: Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis

Honorable Mention

Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis

Arran Zeyu Wang - University of North Carolina-Chapel Hill, Chapel Hill, United States

David Borland - UNC-Chapel Hill, Chapel Hill, United States

David Gotz - University of North Carolina, Chapel Hill, United States

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Room: Bayshore V

2024-10-17T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T12:30:00Z
Exemplar figure, described by caption below
The proposed counterfactual guidance technique is compared with traditional correlation-based guidance through five scenarios. Using the example question "Will coffee drinking cause differences in students' grades?", an analyst might compare data based on coffee consumption and grade distributions. The leftmost column lists the subsets created, and charts illustrate five potential distribution combinations (a-e), suggesting different answers. Symbols at the bottom indicate which methods accurately interpret the data. Counterfactual-based approaches have advantages in two scenarios and perform equally in the other three.
Fast forward
Keywords

Counterfactual, Guidance, Exploratory visual analysis, Visual causal inference, Correlation

Abstract

Providing effective guidance for users has long been an important and challenging task for efficient exploratory visual analytics, especially when selecting variables for visualization in high-dimensional datasets. Correlation is the most widely applied metric for guidance in statistical and analytical tools, however a reliance on correlation may lead users towards false positives when interpreting causal relations in the data. In this work, inspired by prior insights on the benefits of counterfactual visualization in supporting visual causal inference, we propose a novel, simple, and efficient counterfactual guidance method to enhance causal inference performance in guided exploratory analytics based on insights and concerns gathered from expert interviews. Our technique aims to capitalize on the benefits of counterfactual approaches while reducing their complexity for users. We integrated counterfactual guidance into an exploratory visual analytics system, and using a synthetically generated ground-truth causal dataset, conducted a comparative user study and evaluated to what extent counterfactual guidance can help lead users to more precise visual causal inferences. The results suggest that counterfactual guidance improved visual causal inference performance, and also led to different exploratory behaviors compared to correlation-based guidance. Based on these findings, we offer future directions and challenges for incorporating counterfactual guidance to better support exploratory visual analytics.

IEEE VIS 2024 Content: UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization

UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization

Nikolaus Piccolotto - TU Wien, Vienna, Austria

Markus Wallinger - TU Wien, Vienna, Austria

Silvia Miksch - Institute of Visual Computing and Human-Centered Technology, Vienna, Austria

Markus Bögl - TU Wien, Vienna, Austria

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Room: Bayshore V

2024-10-16T14:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:15:00Z
Exemplar figure, described by caption below
The main component of our visualization approach is UnDRground Tubes, which presents glyphs in a grid and connects them by lines according to their set memberships.
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Keywords

Geographical data, multivariate data, set visualization, visual cluster analysis.

Abstract

In various scientific and industrial domains, analyzing multivariate spatial data, i.e., vectors associated with spatial locations, is common practice. To analyze those datasets, analysts may turn to methods such as Spatial Blind Source Separation (SBSS). Designed explicitly for spatial data analysis, SBSS finds latent components in the dataset and is superior to popular non-spatial methods, like PCA. However, when analysts try different tuning parameter settings, the amount of latent components complicates analytical tasks. Based on our years-long collaboration with SBSS researchers, we propose a visualization approach to tackle this challenge. The main component is UnDRground Tubes (UT), a general-purpose idiom combining ideas from set visualization and multidimensional projections. We describe the UT visualization pipeline and integrate UT into an interactive multiple-view system. We demonstrate its effectiveness through interviews with SBSS experts, a qualitative evaluation with visualization experts, and computational experiments. SBSS experts were excited about our approach. They saw many benefits for their work and potential applications for geostatistical data analysis more generally. UT was also well received by visualization experts. Our benchmarks show that UT projections and its heuristics are appropriate.

IEEE VIS 2024 Content: UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization

UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization

Nikolaus Piccolotto - TU Wien, Vienna, Austria

Markus Wallinger - TU Wien, Vienna, Austria

Silvia Miksch - Institute of Visual Computing and Human-Centered Technology, Vienna, Austria

Markus Bögl - TU Wien, Vienna, Austria

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Room: Bayshore V

2024-10-16T14:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:15:00Z
Exemplar figure, described by caption below
The main component of our visualization approach is UnDRground Tubes, which presents glyphs in a grid and connects them by lines according to their set memberships.
Fast forward
Keywords

Geographical data, multivariate data, set visualization, visual cluster analysis.

Abstract

In various scientific and industrial domains, analyzing multivariate spatial data, i.e., vectors associated with spatial locations, is common practice. To analyze those datasets, analysts may turn to methods such as Spatial Blind Source Separation (SBSS). Designed explicitly for spatial data analysis, SBSS finds latent components in the dataset and is superior to popular non-spatial methods, like PCA. However, when analysts try different tuning parameter settings, the amount of latent components complicates analytical tasks. Based on our years-long collaboration with SBSS researchers, we propose a visualization approach to tackle this challenge. The main component is UnDRground Tubes (UT), a general-purpose idiom combining ideas from set visualization and multidimensional projections. We describe the UT visualization pipeline and integrate UT into an interactive multiple-view system. We demonstrate its effectiveness through interviews with SBSS experts, a qualitative evaluation with visualization experts, and computational experiments. SBSS experts were excited about our approach. They saw many benefits for their work and potential applications for geostatistical data analysis more generally. UT was also well received by visualization experts. Our benchmarks show that UT projections and its heuristics are appropriate.

IEEE VIS 2024 Content: PREVis: Perceived Readability Evaluation for Visualizations

Honorable Mention

PREVis: Perceived Readability Evaluation for Visualizations

Anne-Flore Cabouat - LISN, Université Paris Saclay, CNRS, Orsay, France. Aviz, Inria, Saclay, France

Tingying He - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Petra Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Tobias Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

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Room: Bayshore I + II + III

2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
PREVis is a reliable instrument that allows respondents to rate how readable they find a static data visualization across 4 dimensions: layout clarity, ease of understanding, ease of reading data features, and ease of reading data values.
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Keywords

Visualization, readability, validated instrument, perception, user experiments, empirical methods, methodology

Abstract

We developed and validated an instrument to measure the perceived readability in data visualization: PREVis. Researchers and practitioners can easily use this instrument as part of their evaluations to compare the perceived readability of different visual data representations. Our instrument can complement results from controlled experiments on user task performance or provide additional data during in-depth qualitative work such as design iterations when developing a new technique. Although readability is recognized as an essential quality of data visualizations, so far there has not been a unified definition of the construct in the context of visual representations. As a result, researchers often lack guidance for determining how to ask people to rate their perceived readability of a visualization. To address this issue, we engaged in a rigorous process to develop the first validated instrument targeted at the subjective readability of visual data representations. Our final instrument consists of 11 items across 4 dimensions: understandability, layout clarity, readability of data values, and readability of data patterns. We provide the questionnaire as a document with implementation guidelines on osf.io/9cg8j. Beyond this instrument, we contribute a discussion of how researchers have previously assessed visualization readability, and an analysis of the factors underlying perceived readability in visual data representations.

IEEE VIS 2024 Content: PREVis: Perceived Readability Evaluation for Visualizations

Honorable Mention

PREVis: Perceived Readability Evaluation for Visualizations

Anne-Flore Cabouat - LISN, Université Paris Saclay, CNRS, Orsay, France. Aviz, Inria, Saclay, France

Tingying He - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Petra Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Tobias Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

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Room: Bayshore I + II + III

2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
PREVis is a reliable instrument that allows respondents to rate how readable they find a static data visualization across 4 dimensions: layout clarity, ease of understanding, ease of reading data features, and ease of reading data values.
Fast forward
Keywords

Visualization, readability, validated instrument, perception, user experiments, empirical methods, methodology

Abstract

We developed and validated an instrument to measure the perceived readability in data visualization: PREVis. Researchers and practitioners can easily use this instrument as part of their evaluations to compare the perceived readability of different visual data representations. Our instrument can complement results from controlled experiments on user task performance or provide additional data during in-depth qualitative work such as design iterations when developing a new technique. Although readability is recognized as an essential quality of data visualizations, so far there has not been a unified definition of the construct in the context of visual representations. As a result, researchers often lack guidance for determining how to ask people to rate their perceived readability of a visualization. To address this issue, we engaged in a rigorous process to develop the first validated instrument targeted at the subjective readability of visual data representations. Our final instrument consists of 11 items across 4 dimensions: understandability, layout clarity, readability of data values, and readability of data patterns. We provide the questionnaire as a document with implementation guidelines on osf.io/9cg8j. Beyond this instrument, we contribute a discussion of how researchers have previously assessed visualization readability, and an analysis of the factors underlying perceived readability in visual data representations.

IEEE VIS 2024 Content: Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

Tushar M. Athawale - Oak Ridge National Laboratory, Oak Ridge, United States

Zhe Wang - Oak Ridge National Laboratory, Oak Ridge, United States

David Pugmire - Oak Ridge National Laboratory, Oak Ridge, United States

Kenneth Moreland - Oak Ridge National Laboratory, Oak Ridge, United States

Qian Gong - Oak Ridge National Laboratory, Oak Ridge, United States

Scott Klasky - Oak Ridge National Laboratory, Oak Ridge, United States

Chris R. Johnson - University of Utah, Salt Lake City, United States

Paul Rosen - University of Utah, Salt Lake City, United States

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Room: Bayshore VI

2024-10-18T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:06:00Z
Exemplar figure, described by caption below
Critical point visualization for the climate dataset. (a) Critical points of the original data are visualized with blue spheres. (b) Noise in the data creates new critical points for which no uncertainty is visualized. (c) Critical point uncertainty is computed and visualized through elevation proportional to critical point probability. Our closed-form solutions implemented with the VTK-m library provide a 1646x speed-up compared to the conventional approach.
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Keywords

Topology, uncertainty, critical points, probabilistic analysis

Abstract

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. In this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets.

IEEE VIS 2024 Content: Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

Tushar M. Athawale - Oak Ridge National Laboratory, Oak Ridge, United States

Zhe Wang - Oak Ridge National Laboratory, Oak Ridge, United States

David Pugmire - Oak Ridge National Laboratory, Oak Ridge, United States

Kenneth Moreland - Oak Ridge National Laboratory, Oak Ridge, United States

Qian Gong - Oak Ridge National Laboratory, Oak Ridge, United States

Scott Klasky - Oak Ridge National Laboratory, Oak Ridge, United States

Chris R. Johnson - University of Utah, Salt Lake City, United States

Paul Rosen - University of Utah, Salt Lake City, United States

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Room: Bayshore VI

2024-10-18T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:06:00Z
Exemplar figure, described by caption below
Critical point visualization for the climate dataset. (a) Critical points of the original data are visualized with blue spheres. (b) Noise in the data creates new critical points for which no uncertainty is visualized. (c) Critical point uncertainty is computed and visualized through elevation proportional to critical point probability. Our closed-form solutions implemented with the VTK-m library provide a 1646x speed-up compared to the conventional approach.
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Keywords

Topology, uncertainty, critical points, probabilistic analysis

Abstract

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. In this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets.

IEEE VIS 2024 Content: What Can Interactive Visualization do for Participatory Budgeting in Chicago?

What Can Interactive Visualization do for Participatory Budgeting in Chicago?

Alex Kale - University of Chicago, Chicago, United States

Danni Liu - University of Chicago, Chicago, United States

Maria Gabriela Ayala - University of Chicago, Chicago, United States

Harper Schwab - University of Chicago, Chicago, United States

Andrew M McNutt - University of Washington, Seattle, United States. University of Utah, Salt Lake City, United States

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Room: Bayshore II

2024-10-17T18:09:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:09:00Z
Exemplar figure, described by caption below
An illustration of the application scenario for this work, participatory budgeting in Chicago. We investigate the roles that visualization can play in voting on how municipal funding should be spent on neighborhood projects and reporting results of the participatory budgeting vote to stakeholders.
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Keywords

Visualization, Preference elicitation, Digital democracy

Abstract

Participatory budgeting (PB) is a democratic approach to allocating municipal spending that has been adopted in many places in recent years, including in Chicago. Current PB voting resembles a ballot where residents are asked which municipal projects, such as school improvements and road repairs, to fund with a limited budget. In this work, we ask how interactive visualization can benefit PB by conducting a design probe-based interview study (N=13) with policy workers and academics with expertise in PB, urban planning, and civic HCI. Our probe explores how graphical elicitation of voter preferences and a dashboard of voting statistics can be incorporated into a realistic PB tool. Through qualitative analysis, we find that visualization creates opportunities for city government to set expectations about budget constraints while also granting their constituents greater freedom to articulate a wider range of preferences. However, using visualization to provide transparency about PB requires efforts to mitigate potential access barriers and mistrust. We call for more visualization professionals to help build civic capacity by working in and studying political systems.

IEEE VIS 2024 Content: What Can Interactive Visualization do for Participatory Budgeting in Chicago?

What Can Interactive Visualization do for Participatory Budgeting in Chicago?

Alex Kale - University of Chicago, Chicago, United States

Danni Liu - University of Chicago, Chicago, United States

Maria Gabriela Ayala - University of Chicago, Chicago, United States

Harper Schwab - University of Chicago, Chicago, United States

Andrew M McNutt - University of Washington, Seattle, United States. University of Utah, Salt Lake City, United States

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Room: Bayshore II

2024-10-17T18:09:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:09:00Z
Exemplar figure, described by caption below
An illustration of the application scenario for this work, participatory budgeting in Chicago. We investigate the roles that visualization can play in voting on how municipal funding should be spent on neighborhood projects and reporting results of the participatory budgeting vote to stakeholders.
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Keywords

Visualization, Preference elicitation, Digital democracy

Abstract

Participatory budgeting (PB) is a democratic approach to allocating municipal spending that has been adopted in many places in recent years, including in Chicago. Current PB voting resembles a ballot where residents are asked which municipal projects, such as school improvements and road repairs, to fund with a limited budget. In this work, we ask how interactive visualization can benefit PB by conducting a design probe-based interview study (N=13) with policy workers and academics with expertise in PB, urban planning, and civic HCI. Our probe explores how graphical elicitation of voter preferences and a dashboard of voting statistics can be incorporated into a realistic PB tool. Through qualitative analysis, we find that visualization creates opportunities for city government to set expectations about budget constraints while also granting their constituents greater freedom to articulate a wider range of preferences. However, using visualization to provide transparency about PB requires efforts to mitigate potential access barriers and mistrust. We call for more visualization professionals to help build civic capacity by working in and studying political systems.

IEEE VIS 2024 Content: The Effect of Visual Aids on Reading Numeric Data Tables

The Effect of Visual Aids on Reading Numeric Data Tables

YongFeng Ji - University of Victoria, Victoria, Canada

Charles Perin - University of Victoria, Victoria, Canada

Miguel A Nacenta - University of Victoria, Victoria, Canada

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Room: Bayshore II

2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:12:00Z
Exemplar figure, described by caption below
We study the effects of one visual feature (zebra stripping, top right) and two visual encodings (color shading, bottom left, and data bars, bottom right) on the readability of numeric data tables, compared to a plain table (top left).
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Keywords

Data Table, Visual Encoding, Visual Aid, Gaze Analysis, Zebra, Data Bars, Tabular Representations.

Abstract

Data tables are one of the most common ways in which people encounter data. Although mostly built with text and numbers, data tables have a spatial layout and often exhibit visual elements meant to facilitate their reading. Surprisingly, there is an empirical knowledge gap on how people read tables and how different visual aids affect people's reading of tables. In this work, we seek to address this vacuum through a controlled study. We asked participants to repeatedly perform four different tasks with four table representation conditions (plain tables, tables with zebra striping, tables with cell background color encoding cell value, and tables with in-cell bars with lengths encoding cell value). We analyzed completion time, error rate, gaze-tracking data, mouse movement and participant preferences. We found that color and bar encodings help for finding maximum values. For a more complex task (comparison of proportional differences) color and bar helped less than zebra striping. We also characterize typical human behavior for the four tasks. These findings inform the design of tables and research directions for improving presentation of data in tabular form.

IEEE VIS 2024 Content: The Effect of Visual Aids on Reading Numeric Data Tables

The Effect of Visual Aids on Reading Numeric Data Tables

YongFeng Ji - University of Victoria, Victoria, Canada

Charles Perin - University of Victoria, Victoria, Canada

Miguel A Nacenta - University of Victoria, Victoria, Canada

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Room: Bayshore II

2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:12:00Z
Exemplar figure, described by caption below
We study the effects of one visual feature (zebra stripping, top right) and two visual encodings (color shading, bottom left, and data bars, bottom right) on the readability of numeric data tables, compared to a plain table (top left).
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Keywords

Data Table, Visual Encoding, Visual Aid, Gaze Analysis, Zebra, Data Bars, Tabular Representations.

Abstract

Data tables are one of the most common ways in which people encounter data. Although mostly built with text and numbers, data tables have a spatial layout and often exhibit visual elements meant to facilitate their reading. Surprisingly, there is an empirical knowledge gap on how people read tables and how different visual aids affect people's reading of tables. In this work, we seek to address this vacuum through a controlled study. We asked participants to repeatedly perform four different tasks with four table representation conditions (plain tables, tables with zebra striping, tables with cell background color encoding cell value, and tables with in-cell bars with lengths encoding cell value). We analyzed completion time, error rate, gaze-tracking data, mouse movement and participant preferences. We found that color and bar encodings help for finding maximum values. For a more complex task (comparison of proportional differences) color and bar helped less than zebra striping. We also characterize typical human behavior for the four tasks. These findings inform the design of tables and research directions for improving presentation of data in tabular form.

IEEE VIS 2024 Content: Mixing Linters with GUIs: A Color Palette Design Probe

Mixing Linters with GUIs: A Color Palette Design Probe

Andrew M McNutt - University of Washington, Seattle, United States. University of Utah, Salt Lake City, United States

Maureen Stone - University of Washington, Seattle, United States

Jeffrey Heer - University of Washington, Seattle, United States

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Room: Bayshore II

2024-10-16T17:57:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:57:00Z
Exemplar figure, described by caption below
How do you know when what you’ve done is right? Visualization linters provide concrete feedback about chart designs, but so far they have had interface issues that have limited their usefulness. This work introduces a linter (PaletteLint) for color palettes (and a GUI called Color Buddy, pictured here) that explores ways to deal with these issues.
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Keywords

Linters, Color Palette Design, Design Probe, Reflection

Abstract

Visualization linters are end-user facing evaluators that automatically identify potential chart issues. These spell-checker like systems offer a blend of interpretability and customization that is not found in other forms of automated assistance. However, existing linters do not model context and have primarily targeted users who do not need assistance, resulting in obvious---even annoying---advice. We investigate these issues within the domain of color palette design, which serves as a microcosm of visualization design concerns. We contribute a GUI-based color palette linter as a design probe that covers perception, accessibility, context, and other design criteria, and use it to explore visual explanations, integrated fixes, and user defined linting rules. Through a formative interview study and theory-driven analysis, we find that linters can be meaningfully integrated into graphical contextsthereby addressing many of their core issues.We discuss implications for integrating linters into visualization tools, developing improved assertion languages, and supporting end-user tunable advice---all laying the groundwork for more effective visualization linters in any context.

IEEE VIS 2024 Content: Mixing Linters with GUIs: A Color Palette Design Probe

Mixing Linters with GUIs: A Color Palette Design Probe

Andrew M McNutt - University of Washington, Seattle, United States. University of Utah, Salt Lake City, United States

Maureen Stone - University of Washington, Seattle, United States

Jeffrey Heer - University of Washington, Seattle, United States

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Room: Bayshore II

2024-10-16T17:57:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:57:00Z
Exemplar figure, described by caption below
How do you know when what you’ve done is right? Visualization linters provide concrete feedback about chart designs, but so far they have had interface issues that have limited their usefulness. This work introduces a linter (PaletteLint) for color palettes (and a GUI called Color Buddy, pictured here) that explores ways to deal with these issues.
Fast forward
Keywords

Linters, Color Palette Design, Design Probe, Reflection

Abstract

Visualization linters are end-user facing evaluators that automatically identify potential chart issues. These spell-checker like systems offer a blend of interpretability and customization that is not found in other forms of automated assistance. However, existing linters do not model context and have primarily targeted users who do not need assistance, resulting in obvious---even annoying---advice. We investigate these issues within the domain of color palette design, which serves as a microcosm of visualization design concerns. We contribute a GUI-based color palette linter as a design probe that covers perception, accessibility, context, and other design criteria, and use it to explore visual explanations, integrated fixes, and user defined linting rules. Through a formative interview study and theory-driven analysis, we find that linters can be meaningfully integrated into graphical contextsthereby addressing many of their core issues.We discuss implications for integrating linters into visualization tools, developing improved assertion languages, and supporting end-user tunable advice---all laying the groundwork for more effective visualization linters in any context.

IEEE VIS 2024 Content: Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Carter Blair - University of Waterloo, Waterloo, Canada. University of Victoria, Victoria, Canada

Xiyao Wang - University of Victoria, Victoira, Canada. Delft University of Technology, Delft, Netherlands

Charles Perin - University of Victoria, Victoria, Canada

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Room: Bayshore II

2024-10-16T16:24:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:24:00Z
Exemplar figure, described by caption below
We quantify through five studies the effects of color (Study 1 and Study 2), chart type (Study 3, Study 4, and Study 5), data trend (Study 2 and Study 3), data variance (Study 4), and data density (Study 5) on emotion (measured through arousal and valence ratings using the Self-Assessment Manikin scale).
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Keywords

Affect, Data Visualization, Emotion, Quantitative Study

Abstract

Emotion is an important factor to consider when designing visualizations as it can impact the amount of trust viewers place in a visualization, how well they can retrieve information and understand the underlying data, and how much they engage with or connect to a visualization. We conducted five crowdsourced experiments to quantify the effects of color, chart type, data trend, data variability and data density on emotion (measured through self-reported arousal and valence). Results from our experiments show that there are multiple design elements which influence the emotion induced by a visualization and, more surprisingly, that certain data characteristics influence the emotion of viewers even when the data has no meaning. In light of these findings, we offer guidelines on how to use color, scale, and chart type to counterbalance and emphasize the emotional impact of immutable data characteristics.

IEEE VIS 2024 Content: Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Carter Blair - University of Waterloo, Waterloo, Canada. University of Victoria, Victoria, Canada

Xiyao Wang - University of Victoria, Victoira, Canada. Delft University of Technology, Delft, Netherlands

Charles Perin - University of Victoria, Victoria, Canada

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Room: Bayshore II

2024-10-16T16:24:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:24:00Z
Exemplar figure, described by caption below
We quantify through five studies the effects of color (Study 1 and Study 2), chart type (Study 3, Study 4, and Study 5), data trend (Study 2 and Study 3), data variance (Study 4), and data density (Study 5) on emotion (measured through arousal and valence ratings using the Self-Assessment Manikin scale).
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Keywords

Affect, Data Visualization, Emotion, Quantitative Study

Abstract

Emotion is an important factor to consider when designing visualizations as it can impact the amount of trust viewers place in a visualization, how well they can retrieve information and understand the underlying data, and how much they engage with or connect to a visualization. We conducted five crowdsourced experiments to quantify the effects of color, chart type, data trend, data variability and data density on emotion (measured through self-reported arousal and valence). Results from our experiments show that there are multiple design elements which influence the emotion induced by a visualization and, more surprisingly, that certain data characteristics influence the emotion of viewers even when the data has no meaning. In light of these findings, we offer guidelines on how to use color, scale, and chart type to counterbalance and emphasize the emotional impact of immutable data characteristics.

IEEE VIS 2024 Content: A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space

Md Dilshadur Rahman - University of Utah, Salt Lake City, United States. University of Utah, Salt Lake City, United States

Ghulam Jilani Quadri - University of Oklahoma, Norman, United States. University of Oklahoma, Norman, United States

Bhavana Doppalapudi - University of South Florida , Tampa, United States. University of South Florida , Tampa, United States

Danielle Albers Szafir - University of North Carolina-Chapel Hill, Chapel Hill, United States. University of North Carolina-Chapel Hill, Chapel Hill, United States

Paul Rosen - University of Utah, Salt Lake City, United States. University of Utah, Salt Lake City, United States

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Room: Bayshore V

2024-10-16T12:42:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:42:00Z
Exemplar figure, described by caption below
A line chart from The Washington Post illustrates COVID-19 peak comparisons, plotting time on the horizontal axis and percentage growth relative to the January 2021 peak vertically: top-left shows the baseline chart with basic visualization elements (i.e., axes, labels, lines, legends, and gridlines) but with annotations removed; top-right uses color+enclosure+text ensembles of annotations to help identify the peaks of different COVID-19 waves; bottom-left uses text+connector ensembles to present additional context from the associated article; and bottom-right displays the completely annotated chart.
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Keywords

Annotations, visualizations, qualitative study, design space, taxonomy

Abstract

Annotations play a vital role in highlighting critical aspects of visualizations, aiding in data externalization and exploration, collaborative sensemaking, and visual storytelling. However, despite their widespread use, we identified a lack of a design space for common practices for annotations. In this paper, we evaluated over 1,800 static annotated charts to understand how people annotate visualizations in practice. Through qualitative coding of these diverse real-world annotated charts, we explored three primary aspects of annotation usage patterns: analytic purposes for chart annotations (e.g., present, identify, summarize, or compare data features), mechanisms for chart annotations (e.g., types and combinations of annotations used, frequency of different annotation types across chart types, etc.), and the data source used to generate the annotations. We then synthesized our findings into a design space of annotations, highlighting key design choices for chart annotations. We presented three case studies illustrating our design space as a practical framework for chart annotations to enhance the communication of visualization insights. All supplemental materials are available at \url{https://shorturl.at/bAGM1}.

IEEE VIS 2024 Content: A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space

Md Dilshadur Rahman - University of Utah, Salt Lake City, United States. University of Utah, Salt Lake City, United States

Ghulam Jilani Quadri - University of Oklahoma, Norman, United States. University of Oklahoma, Norman, United States

Bhavana Doppalapudi - University of South Florida , Tampa, United States. University of South Florida , Tampa, United States

Danielle Albers Szafir - University of North Carolina-Chapel Hill, Chapel Hill, United States. University of North Carolina-Chapel Hill, Chapel Hill, United States

Paul Rosen - University of Utah, Salt Lake City, United States. University of Utah, Salt Lake City, United States

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Room: Bayshore V

2024-10-16T12:42:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:42:00Z
Exemplar figure, described by caption below
A line chart from The Washington Post illustrates COVID-19 peak comparisons, plotting time on the horizontal axis and percentage growth relative to the January 2021 peak vertically: top-left shows the baseline chart with basic visualization elements (i.e., axes, labels, lines, legends, and gridlines) but with annotations removed; top-right uses color+enclosure+text ensembles of annotations to help identify the peaks of different COVID-19 waves; bottom-left uses text+connector ensembles to present additional context from the associated article; and bottom-right displays the completely annotated chart.
Fast forward
Keywords

Annotations, visualizations, qualitative study, design space, taxonomy

Abstract

Annotations play a vital role in highlighting critical aspects of visualizations, aiding in data externalization and exploration, collaborative sensemaking, and visual storytelling. However, despite their widespread use, we identified a lack of a design space for common practices for annotations. In this paper, we evaluated over 1,800 static annotated charts to understand how people annotate visualizations in practice. Through qualitative coding of these diverse real-world annotated charts, we explored three primary aspects of annotation usage patterns: analytic purposes for chart annotations (e.g., present, identify, summarize, or compare data features), mechanisms for chart annotations (e.g., types and combinations of annotations used, frequency of different annotation types across chart types, etc.), and the data source used to generate the annotations. We then synthesized our findings into a design space of annotations, highlighting key design choices for chart annotations. We presented three case studies illustrating our design space as a practical framework for chart annotations to enhance the communication of visualization insights. All supplemental materials are available at \url{https://shorturl.at/bAGM1}.

IEEE VIS 2024 Content: Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics

Honorable Mention

Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics

Gabriela Molina León - University of Bremen, Bremen, Germany. University of Bremen, Bremen, Germany

Anastasia Bezerianos - LISN, Université Paris-Saclay, CNRS, INRIA, Orsay, France

Olivier Gladin - Inria, Palaiseau, France

Petra Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

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Room: Bayshore V

2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:00:00Z
Exemplar figure, described by caption below
Two people standing in front of the wall display; one person is moving a group of selected documents by dragging a stack of them with the index finger while the other one observes.
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Keywords

Speech interaction, wall display, collaborative sensemaking, multimodal interaction, collaboration styles

Abstract

We present the results of an exploratory study on how pairs interact with speech commands and touch gestures on a wall-sized display during a collaborative sensemaking task. Previous work has shown that speech commands, alone or in combination with other input modalities, can support visual data exploration by individuals. However, it is still unknown whether and how speech commands can be used in collaboration, and for what tasks. To answer these questions, we developed a functioning prototype that we used as a technology probe. We conducted an in-depth exploratory study with 10 participant pairs to analyze their interaction choices, the interplay between the input modalities, and their collaboration. While touch was the most used modality, we found that participants preferred speech commands for global operations, used them for distant interaction, and that speech interaction contributed to the awareness of the partner’s actions. Furthermore, the likelihood of using speech commands during collaboration was related to the personality trait of agreeableness. Regarding collaboration styles, participants interacted with speech equally often whether they were in loosely or closely coupled collaboration. While the partners stood closer to each other during close collaboration, they did not distance themselves to use speech commands. From our findings, we derive and contribute a set of design considerations for collaborative and multimodal interactive data analysis systems. All supplemental materials are available at https://osf.io/8gpv2.

IEEE VIS 2024 Content: Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics

Honorable Mention

Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics

Gabriela Molina León - University of Bremen, Bremen, Germany. University of Bremen, Bremen, Germany

Anastasia Bezerianos - LISN, Université Paris-Saclay, CNRS, INRIA, Orsay, France

Olivier Gladin - Inria, Palaiseau, France

Petra Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

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Room: Bayshore V

2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:00:00Z
Exemplar figure, described by caption below
Two people standing in front of the wall display; one person is moving a group of selected documents by dragging a stack of them with the index finger while the other one observes.
Fast forward
Keywords

Speech interaction, wall display, collaborative sensemaking, multimodal interaction, collaboration styles

Abstract

We present the results of an exploratory study on how pairs interact with speech commands and touch gestures on a wall-sized display during a collaborative sensemaking task. Previous work has shown that speech commands, alone or in combination with other input modalities, can support visual data exploration by individuals. However, it is still unknown whether and how speech commands can be used in collaboration, and for what tasks. To answer these questions, we developed a functioning prototype that we used as a technology probe. We conducted an in-depth exploratory study with 10 participant pairs to analyze their interaction choices, the interplay between the input modalities, and their collaboration. While touch was the most used modality, we found that participants preferred speech commands for global operations, used them for distant interaction, and that speech interaction contributed to the awareness of the partner’s actions. Furthermore, the likelihood of using speech commands during collaboration was related to the personality trait of agreeableness. Regarding collaboration styles, participants interacted with speech equally often whether they were in loosely or closely coupled collaboration. While the partners stood closer to each other during close collaboration, they did not distance themselves to use speech commands. From our findings, we derive and contribute a set of design considerations for collaborative and multimodal interactive data analysis systems. All supplemental materials are available at https://osf.io/8gpv2.

IEEE VIS 2024 Content: BEMTrace: Visualization-driven approach for deriving Building Energy Models from BIM

BEMTrace: Visualization-driven approach for deriving Building Energy Models from BIM

Andreas Walch - VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria

Attila Szabo - VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria

Harald Steinlechner - VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria

Thomas Ortner - Independent Researcher, Vienna, Austria

Eduard Gröller - Institute of Visual Computing . Human-Centered Technology, Vienna, Austria

Johanna Schmidt - VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria

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Room: Bayshore VII

2024-10-16T14:27:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:27:00Z
Exemplar figure, described by caption below
BEMTrace enhances the data curation process from a Building Information Model (BIM) to a Building Energy Model (BEM) by providing visual support for the BIM-to-BEM conversion. Users can access various views to better understand the complex data transformation, including the BIM World, BEM World, and the Relationship View, which illustrates the transition between them. Context-adaptive selections assist users in navigating these views, allowing for detailed exploration of different data aspects. This approach ensures a clearer understanding of the conversion process and helps in resolving any arising conflicts.
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Keywords

BIM, BEM, BIM-to-BEM, 3D Data Wrangling, 3D selections, Visualization for trust building

Abstract

Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a basis for thermal simulations to assess the building’s energy performance. This paper explores the intersection of BIM and BEM, focusing on the challenges and methodologies in converting BIM data into BEM representations for energy performance analysis. BEMTrace integrates 3D data wrangling techniques with visualization methodologies to enhance the accuracy and traceability of the BIM-to-BEM conversion process. Through parsing, error detection, and algorithmic correction of BIM data, our methods generate valid BEM models suitable for energy simulation. Visualization techniques provide transparent insights into the conversion process, aiding error identification, validation, and user comprehension. We introduce context-adaptive selections to facilitate user interaction and to show that the BEMTrace workflow helps users understand complex 3D data wrangling processes.

IEEE VIS 2024 Content: BEMTrace: Visualization-driven approach for deriving Building Energy Models from BIM

BEMTrace: Visualization-driven approach for deriving Building Energy Models from BIM

Andreas Walch - VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria

Attila Szabo - VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria

Harald Steinlechner - VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria

Thomas Ortner - Independent Researcher, Vienna, Austria

Eduard Gröller - Institute of Visual Computing . Human-Centered Technology, Vienna, Austria

Johanna Schmidt - VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria

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Room: Bayshore VII

2024-10-16T14:27:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T14:27:00Z
Exemplar figure, described by caption below
BEMTrace enhances the data curation process from a Building Information Model (BIM) to a Building Energy Model (BEM) by providing visual support for the BIM-to-BEM conversion. Users can access various views to better understand the complex data transformation, including the BIM World, BEM World, and the Relationship View, which illustrates the transition between them. Context-adaptive selections assist users in navigating these views, allowing for detailed exploration of different data aspects. This approach ensures a clearer understanding of the conversion process and helps in resolving any arising conflicts.
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Keywords

BIM, BEM, BIM-to-BEM, 3D Data Wrangling, 3D selections, Visualization for trust building

Abstract

Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a basis for thermal simulations to assess the building’s energy performance. This paper explores the intersection of BIM and BEM, focusing on the challenges and methodologies in converting BIM data into BEM representations for energy performance analysis. BEMTrace integrates 3D data wrangling techniques with visualization methodologies to enhance the accuracy and traceability of the BIM-to-BEM conversion process. Through parsing, error detection, and algorithmic correction of BIM data, our methods generate valid BEM models suitable for energy simulation. Visualization techniques provide transparent insights into the conversion process, aiding error identification, validation, and user comprehension. We introduce context-adaptive selections to facilitate user interaction and to show that the BEMTrace workflow helps users understand complex 3D data wrangling processes.

IEEE VIS 2024 Content: VMC: A Grammar for Visualizing Statistical Model Checks

VMC: A Grammar for Visualizing Statistical Model Checks

Ziyang Guo - Northwestern University, Evanston, United States

Alex Kale - University of Chicago, Chicago, United States

Matthew Kay - Northwestern University, Chicago, United States

Jessica Hullman - Northwestern University, Evanston, United States

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Room: Bayshore V

2024-10-17T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T12:54:00Z
Exemplar figure, described by caption below
Example model check visualizations authored with VMC, using data from [ 46 ]. From left to right: checks on the density curves of the distributions of model predictions and observed data from (A) response variable to (B) distributional parameter; follow-up checks conditional on the quantitative predictor, where VMC is used to specify (C) Hypothetical Outcome Plots and (D) a line + ribbon plot; (E) a facet check stratifying the random effects and (F) a multilevel check; more checks for the random effects specified by VMC, including (G) raincloud plots and (H) multiple-interval plots; and residual checks specified by VMC, including (I) residual plots revealing the heteroskedasticity of the model and (J) Q-Q plots, validating the normality of residuals.
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Keywords

Model checking and evaluation; Uncertainty visualization; Grammar of Graphics

Abstract

Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model checks. VMC defines a model check visualization using four components: (1) samples of distributions of checkable quantities generated from the model,including predictive distributions for new data and distributions of model parameters; (2) transformations on observed data to facilitate comparison; (3) visual representations of distributions; and (4) layouts to facilitate comparing model samples and observed data. We contribute an implementation of VMC as an R package. We validate VMC by reproducing a set of canonical model check examples, and show how using VMC to generate model checks reduces the edit distance between visualizations relative to existing visualization toolkits. The findings of an interview study with three expert modelers who used VMC highlight challenges and opportunities for encouraging exploration of correct, effective model check visualizations.

IEEE VIS 2024 Content: VMC: A Grammar for Visualizing Statistical Model Checks

VMC: A Grammar for Visualizing Statistical Model Checks

Ziyang Guo - Northwestern University, Evanston, United States

Alex Kale - University of Chicago, Chicago, United States

Matthew Kay - Northwestern University, Chicago, United States

Jessica Hullman - Northwestern University, Evanston, United States

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Room: Bayshore V

2024-10-17T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T12:54:00Z
Exemplar figure, described by caption below
Example model check visualizations authored with VMC, using data from [ 46 ]. From left to right: checks on the density curves of the distributions of model predictions and observed data from (A) response variable to (B) distributional parameter; follow-up checks conditional on the quantitative predictor, where VMC is used to specify (C) Hypothetical Outcome Plots and (D) a line + ribbon plot; (E) a facet check stratifying the random effects and (F) a multilevel check; more checks for the random effects specified by VMC, including (G) raincloud plots and (H) multiple-interval plots; and residual checks specified by VMC, including (I) residual plots revealing the heteroskedasticity of the model and (J) Q-Q plots, validating the normality of residuals.
Fast forward
Keywords

Model checking and evaluation; Uncertainty visualization; Grammar of Graphics

Abstract

Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model checks. VMC defines a model check visualization using four components: (1) samples of distributions of checkable quantities generated from the model,including predictive distributions for new data and distributions of model parameters; (2) transformations on observed data to facilitate comparison; (3) visual representations of distributions; and (4) layouts to facilitate comparing model samples and observed data. We contribute an implementation of VMC as an R package. We validate VMC by reproducing a set of canonical model check examples, and show how using VMC to generate model checks reduces the edit distance between visualizations relative to existing visualization toolkits. The findings of an interview study with three expert modelers who used VMC highlight challenges and opportunities for encouraging exploration of correct, effective model check visualizations.

IEEE VIS 2024 Content: The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling

The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling

Hana Pokojná - Masaryk University, Brno, Czech Republic

Tobias Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Stefan Bruckner - University of Rostock, Rostock, Germany

Barbora Kozlikova - Masaryk University, Brno, Czech Republic

Laura Garrison - University of Bergen, Bergen, Norway. Haukeland University Hospital, University of Bergen, Bergen, Norway

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Room: Bayshore V

2024-10-16T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:54:00Z
Exemplar figure, described by caption below
image_VisualMetaphors This image illustrates our process (from left to right) for identifying and classifying visual conceptual metaphors in scientific infographics: 1) deconstruct a given infographic to its component graphics, 2) identify component graphics as visual conceptual metaphors versus visual abstractions, 3) classify the conceptual metaphor type (structural, ontological, orientational, or imagistic), and 4) provide infographic metadata and classify the spatiotemporal scale of the phenomenon visualized to enable detailed investigation in our Visual Exploratory Tool.
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Keywords

Visualization, visual metaphors, science communication, conceptual metaphors, visual communication

Abstract

We apply an approach from cognitive linguistics by mapping Conceptual Metaphor Theory (CMT) to the visualization domain to address patterns of visual conceptual metaphors that are often used in science infographics. Metaphors play an essential part in visual communication and are frequently employed to explain complex concepts. However, their use is often based on intuition, rather than following a formal process. At present, we lack tools and language for understanding and describing metaphor use in visualization to the extent where taxonomy and grammar could guide the creation of visual components, e.g., infographics. Our classification of the visual conceptual mappings within scientific representations is based on the breakdown of visual components in existing scientific infographics. We demonstrate the development of this mapping through a detailed analysis of data collected from four domains (biomedicine, climate, space, and anthropology) that represent a diverse range of visual conceptual metaphors used in the visual communication of science. This work allows us to identify patterns of visual conceptual metaphor use within the domains, resolve ambiguities about why specific conceptual metaphors are used, and develop a better overall understanding of visual metaphor use in scientific infographics. Our analysis shows that ontological and orientational conceptual metaphors are the most widely applied to translate complex scientific concepts. To support our findings we developed a visual exploratory tool based on the collected database that places the individual infographics on a spatio-temporal scale and illustrates the breakdown of visual conceptual metaphors.

IEEE VIS 2024 Content: The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling

The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling

Hana Pokojná - Masaryk University, Brno, Czech Republic

Tobias Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Stefan Bruckner - University of Rostock, Rostock, Germany

Barbora Kozlikova - Masaryk University, Brno, Czech Republic

Laura Garrison - University of Bergen, Bergen, Norway. Haukeland University Hospital, University of Bergen, Bergen, Norway

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Room: Bayshore V

2024-10-16T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:54:00Z
Exemplar figure, described by caption below
image_VisualMetaphors This image illustrates our process (from left to right) for identifying and classifying visual conceptual metaphors in scientific infographics: 1) deconstruct a given infographic to its component graphics, 2) identify component graphics as visual conceptual metaphors versus visual abstractions, 3) classify the conceptual metaphor type (structural, ontological, orientational, or imagistic), and 4) provide infographic metadata and classify the spatiotemporal scale of the phenomenon visualized to enable detailed investigation in our Visual Exploratory Tool.
Fast forward
Keywords

Visualization, visual metaphors, science communication, conceptual metaphors, visual communication

Abstract

We apply an approach from cognitive linguistics by mapping Conceptual Metaphor Theory (CMT) to the visualization domain to address patterns of visual conceptual metaphors that are often used in science infographics. Metaphors play an essential part in visual communication and are frequently employed to explain complex concepts. However, their use is often based on intuition, rather than following a formal process. At present, we lack tools and language for understanding and describing metaphor use in visualization to the extent where taxonomy and grammar could guide the creation of visual components, e.g., infographics. Our classification of the visual conceptual mappings within scientific representations is based on the breakdown of visual components in existing scientific infographics. We demonstrate the development of this mapping through a detailed analysis of data collected from four domains (biomedicine, climate, space, and anthropology) that represent a diverse range of visual conceptual metaphors used in the visual communication of science. This work allows us to identify patterns of visual conceptual metaphor use within the domains, resolve ambiguities about why specific conceptual metaphors are used, and develop a better overall understanding of visual metaphor use in scientific infographics. Our analysis shows that ontological and orientational conceptual metaphors are the most widely applied to translate complex scientific concepts. To support our findings we developed a visual exploratory tool based on the collected database that places the individual infographics on a spatio-temporal scale and illustrates the breakdown of visual conceptual metaphors.

IEEE VIS 2024 Content: How Good (Or Bad) Are LLMs in Detecting Misleading Visualizations

How Good (Or Bad) Are LLMs in Detecting Misleading Visualizations

Leo Yu-Ho Lo - The Hong Kong University of Science and Technology, Hong Kong, China

Huamin Qu - The Hong Kong University of Science and Technology, Hong Kong, China

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Room: Bayshore I + II + III

2024-10-18T13:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:30:00Z
Exemplar figure, described by caption below
The paper title is "How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?" On the left hand side, the LLM reponse correctly identified the chart as misleading and gave a relevant reason. On the right hand side, the LLM reponse incorrectly and gave a wrong interpretation.
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Keywords

Deceptive Visualization, Large Language Models, Prompt Engineering

Abstract

In this study, we address the growing issue of misleading charts, a prevalent problem that undermines the integrity of information dissemination. Misleading charts can distort the viewer’s perception of data, leading to misinterpretations and decisions based on false information. The development of effective automatic detection methods for misleading charts is an urgent field of research. The recent advancement of multimodal Large Language Models (LLMs) has introduced a promising direction for addressing this challenge. We explored the capabilities of these models in analyzing complex charts and assessing the impact of different prompting strategies on the models’ analyses. We utilized a dataset of misleading charts collected from the internet by prior research and crafted nine distinct prompts, ranging from simple to complex, to test the ability of four different multimodal LLMs in detecting over 21 different chart issues. Through three experiments–from initial exploration to detailed analysis–we progressively gained insights into how to effectively prompt LLMs to identify misleading charts and developed strategies to address the scalability challenges encountered as we expanded our detection range from the initial five issues to 21 issues in the final experiment. Our findings reveal that multimodal LLMs possess a strong capability for chart comprehension and critical thinking in data interpretation. There is significant potential in employing multimodal LLMs to counter misleading information by supporting critical thinking and enhancing visualization literacy. This study demonstrates the applicability of LLMs in addressing the pressing concern of misleading charts.

IEEE VIS 2024 Content: How Good (Or Bad) Are LLMs in Detecting Misleading Visualizations

How Good (Or Bad) Are LLMs in Detecting Misleading Visualizations

Leo Yu-Ho Lo - The Hong Kong University of Science and Technology, Hong Kong, China

Huamin Qu - The Hong Kong University of Science and Technology, Hong Kong, China

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Room: Bayshore I + II + III

2024-10-18T13:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:30:00Z
Exemplar figure, described by caption below
The paper title is "How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?" On the left hand side, the LLM reponse correctly identified the chart as misleading and gave a relevant reason. On the right hand side, the LLM reponse incorrectly and gave a wrong interpretation.
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Keywords

Deceptive Visualization, Large Language Models, Prompt Engineering

Abstract

In this study, we address the growing issue of misleading charts, a prevalent problem that undermines the integrity of information dissemination. Misleading charts can distort the viewer’s perception of data, leading to misinterpretations and decisions based on false information. The development of effective automatic detection methods for misleading charts is an urgent field of research. The recent advancement of multimodal Large Language Models (LLMs) has introduced a promising direction for addressing this challenge. We explored the capabilities of these models in analyzing complex charts and assessing the impact of different prompting strategies on the models’ analyses. We utilized a dataset of misleading charts collected from the internet by prior research and crafted nine distinct prompts, ranging from simple to complex, to test the ability of four different multimodal LLMs in detecting over 21 different chart issues. Through three experiments–from initial exploration to detailed analysis–we progressively gained insights into how to effectively prompt LLMs to identify misleading charts and developed strategies to address the scalability challenges encountered as we expanded our detection range from the initial five issues to 21 issues in the final experiment. Our findings reveal that multimodal LLMs possess a strong capability for chart comprehension and critical thinking in data interpretation. There is significant potential in employing multimodal LLMs to counter misleading information by supporting critical thinking and enhancing visualization literacy. This study demonstrates the applicability of LLMs in addressing the pressing concern of misleading charts.

IEEE VIS 2024 Content: Motion-Based Visual Encoding Can Improve Performance on Perceptual Tasks with Dynamic Time Series

Motion-Based Visual Encoding Can Improve Performance on Perceptual Tasks with Dynamic Time Series

Songwen Hu - Georgia Institute of Technology, Atlanta, United States

Ouxun Jiang - Northwestern University, Evanston, United States

Jeffrey Riedmiller - Dolby Laboratories Inc., San Francisco, United States

Cindy Xiong Bearfield - Georgia Tech, Atlanta, United States. University of Massachusetts Amherst, Amherst, United States

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Room: Bayshore III

2024-10-17T17:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T17:45:00Z
Exemplar figure, described by caption below
Examples of different animation design options. The animations are arranged in a time sequence from top to bottom and categorized into six conditions from left to right.
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Keywords

Animation, Dynamic Displays, Perception, Motion, Analytic Tasks

Abstract

Dynamic data visualizations can convey large amounts of information over time, such as using motion to depict changes in data values for multiple entities. Such dynamic displays put a demand on our visual processing capacities, yet our perception of motion is limited. Several techniques have been shown to improve the processing of dynamic displays. Staging the animation to sequentially show steps in a transition and tracing object movement by displaying trajectory histories can improve processing by reducing the cognitive load. In this paper, We examine the effectiveness of staging and tracing in dynamic displays. We showed participants animated line charts depicting the movements of lines and asked them to identify the line with the highest mean and variance. We manipulated the animation to display the lines with or without staging, tracing and history, and compared the results to a static chart as a control. Results showed that tracing and staging are preferred by participants, and improve their performance in mean and variance tasks respectively. They also preferred display time 3 times shorter when staging is used. Also, encoding animation speed with mean and variance in congruent tasks is associated with higher accuracy. These findings help inform real-world best practices for building dynamic displays. The supplementary materials can be found at https://osf.io/8c95v/

IEEE VIS 2024 Content: Motion-Based Visual Encoding Can Improve Performance on Perceptual Tasks with Dynamic Time Series

Motion-Based Visual Encoding Can Improve Performance on Perceptual Tasks with Dynamic Time Series

Songwen Hu - Georgia Institute of Technology, Atlanta, United States

Ouxun Jiang - Northwestern University, Evanston, United States

Jeffrey Riedmiller - Dolby Laboratories Inc., San Francisco, United States

Cindy Xiong Bearfield - Georgia Tech, Atlanta, United States. University of Massachusetts Amherst, Amherst, United States

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Room: Bayshore III

2024-10-17T17:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T17:45:00Z
Exemplar figure, described by caption below
Examples of different animation design options. The animations are arranged in a time sequence from top to bottom and categorized into six conditions from left to right.
Fast forward
Keywords

Animation, Dynamic Displays, Perception, Motion, Analytic Tasks

Abstract

Dynamic data visualizations can convey large amounts of information over time, such as using motion to depict changes in data values for multiple entities. Such dynamic displays put a demand on our visual processing capacities, yet our perception of motion is limited. Several techniques have been shown to improve the processing of dynamic displays. Staging the animation to sequentially show steps in a transition and tracing object movement by displaying trajectory histories can improve processing by reducing the cognitive load. In this paper, We examine the effectiveness of staging and tracing in dynamic displays. We showed participants animated line charts depicting the movements of lines and asked them to identify the line with the highest mean and variance. We manipulated the animation to display the lines with or without staging, tracing and history, and compared the results to a static chart as a control. Results showed that tracing and staging are preferred by participants, and improve their performance in mean and variance tasks respectively. They also preferred display time 3 times shorter when staging is used. Also, encoding animation speed with mean and variance in congruent tasks is associated with higher accuracy. These findings help inform real-world best practices for building dynamic displays. The supplementary materials can be found at https://osf.io/8c95v/

IEEE VIS 2024 Content: LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models

LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models

Minsuk Kahng - Google, Atlanta, United States

Ian Tenney - Google Research, Seattle, United States

Mahima Pushkarna - Google Research, Cambridge, United States

Michael Xieyang Liu - Google Research, Pittsburgh, United States

James Wexler - Google Research, Cambridge, United States

Emily Reif - Google, Cambridge, United States

Krystal Kallarackal - Google Research, Mountain View, United States

Minsuk Chang - Google Research, Seattle, United States

Michael Terry - Google, Cambridge, United States

Lucas Dixon - Google, Paris, France

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Room: Bayshore V

2024-10-18T12:42:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:42:00Z
Exemplar figure, described by caption below
LLM Comparator is a visual analytics tool consisting of multiple views: an interactive table which displays individual prompts and model responses, and a visualization summary which comprises multiple panels, including score distribution, metrics by prompt category, rationale clusters, n-grams, and custom functions.
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Keywords

Visual analytics, large language models, model evaluation, responsible AI, machine learning interpretability.

Abstract

Evaluating large language models (LLMs) presents unique challenges. While automatic side-by-side evaluation, also known as LLM-as-a-judge, has become a promising solution, model developers and researchers face difficulties with scalability and interpretability when analyzing these evaluation outcomes. To address these challenges, we introduce LLM Comparator, a new visual analytics tool designed for side-by-side evaluations of LLMs. This tool provides analytical workflows that help users understand when and why one LLM outperforms or underperforms another, and how their responses differ. Through close collaboration with practitioners developing LLMs at Google, we have iteratively designed, developed, and refined the tool. Qualitative feedback from these users highlights that the tool facilitates in-depth analysis of individual examples while enabling users to visually overview and flexibly slice data. This empowers users to identify undesirable patterns, formulate hypotheses about model behavior, and gain insights for model improvement. LLM Comparator has been integrated into Google's LLM evaluation platforms and open-sourced.

IEEE VIS 2024 Content: LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models

LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models

Minsuk Kahng - Google, Atlanta, United States

Ian Tenney - Google Research, Seattle, United States

Mahima Pushkarna - Google Research, Cambridge, United States

Michael Xieyang Liu - Google Research, Pittsburgh, United States

James Wexler - Google Research, Cambridge, United States

Emily Reif - Google, Cambridge, United States

Krystal Kallarackal - Google Research, Mountain View, United States

Minsuk Chang - Google Research, Seattle, United States

Michael Terry - Google, Cambridge, United States

Lucas Dixon - Google, Paris, France

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Room: Bayshore V

2024-10-18T12:42:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:42:00Z
Exemplar figure, described by caption below
LLM Comparator is a visual analytics tool consisting of multiple views: an interactive table which displays individual prompts and model responses, and a visualization summary which comprises multiple panels, including score distribution, metrics by prompt category, rationale clusters, n-grams, and custom functions.
Fast forward
Keywords

Visual analytics, large language models, model evaluation, responsible AI, machine learning interpretability.

Abstract

Evaluating large language models (LLMs) presents unique challenges. While automatic side-by-side evaluation, also known as LLM-as-a-judge, has become a promising solution, model developers and researchers face difficulties with scalability and interpretability when analyzing these evaluation outcomes. To address these challenges, we introduce LLM Comparator, a new visual analytics tool designed for side-by-side evaluations of LLMs. This tool provides analytical workflows that help users understand when and why one LLM outperforms or underperforms another, and how their responses differ. Through close collaboration with practitioners developing LLMs at Google, we have iteratively designed, developed, and refined the tool. Qualitative feedback from these users highlights that the tool facilitates in-depth analysis of individual examples while enabling users to visually overview and flexibly slice data. This empowers users to identify undesirable patterns, formulate hypotheses about model behavior, and gain insights for model improvement. LLM Comparator has been integrated into Google's LLM evaluation platforms and open-sourced.

IEEE VIS 2024 Content: StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions

StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions

Zixin Chen - The Hong Kong University of Science and Technology, Hong Kong, China

Jiachen Wang - The Hong Kong University of Science and Technology, Sai Kung, China

Meng Xia - Texas A. M University, College Station, United States

Kento Shigyo - The Hong Kong University of Science and Technology, Kowloon, Hong Kong

Dingdong Liu - The Hong Kong University of Science and Technology, Hong Kong, China

Rong Zhang - Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Huamin Qu - The Hong Kong University of Science and Technology, Hong Kong, China

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Room: Bayshore V

2024-10-16T16:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:00:00Z
Exemplar figure, described by caption below
We developed StuGPTViz, a visual analytics system designed to analyze and compare student interactions with ChatGPT in a master's-level data visualization course. By categorizing prompts and responses using a coding scheme grounded in literature on cognitive levels and thematic analysis, the system reveals key patterns and insights. Validated through expert interviews and case studies, StuGPTViz enhances educators' understanding of ChatGPT's pedagogical value, demonstrating the potential of visual analytics to drive AI-driven personalized learning and improve educational outcomes.
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Keywords

Visual analytics for education, ChatGPT for education, student-ChatGPT interaction

Abstract

The integration of Large Language Models (LLMs), especially ChatGPT, into education is poised to revolutionize students’ learning experiences by introducing innovative conversational learning methodologies. To empower students to fully leverage the capabilities of ChatGPT in educational scenarios, understanding students’ interaction patterns with ChatGPT is crucial for instructors. However, this endeavor is challenging due to the absence of datasets focused on student-ChatGPT conversations and the complexities in identifying and analyzing the evolutional interaction patterns within conversations. To address these challenges, we collected conversational data from 48 students interacting with ChatGPT in a master’s level data visualization course over one semester. We then developed a coding scheme, grounded in the literature on cognitive levels and thematic analysis, to categorize students’ interaction patterns with ChatGPT. Furthermore, we present a visual analytics system, StuGPTViz, that tracks and compares temporal patterns in student prompts and the quality of ChatGPT’s responses at multiple scales, revealing significant pedagogical insights for instructors. We validated the system’s effectiveness through expert interviews with six data visualization instructors and three case studies. The results confirmed StuGPTViz’s capacity to enhance educators’ insights into the pedagogical value of ChatGPT. We also discussed the potential research opportunities of applying visual analytics in education and developing AI-driven personalized learning solutions.

IEEE VIS 2024 Content: StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions

StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions

Zixin Chen - The Hong Kong University of Science and Technology, Hong Kong, China

Jiachen Wang - The Hong Kong University of Science and Technology, Sai Kung, China

Meng Xia - Texas A. M University, College Station, United States

Kento Shigyo - The Hong Kong University of Science and Technology, Kowloon, Hong Kong

Dingdong Liu - The Hong Kong University of Science and Technology, Hong Kong, China

Rong Zhang - Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Huamin Qu - The Hong Kong University of Science and Technology, Hong Kong, China

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Room: Bayshore V

2024-10-16T16:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:00:00Z
Exemplar figure, described by caption below
We developed StuGPTViz, a visual analytics system designed to analyze and compare student interactions with ChatGPT in a master's-level data visualization course. By categorizing prompts and responses using a coding scheme grounded in literature on cognitive levels and thematic analysis, the system reveals key patterns and insights. Validated through expert interviews and case studies, StuGPTViz enhances educators' understanding of ChatGPT's pedagogical value, demonstrating the potential of visual analytics to drive AI-driven personalized learning and improve educational outcomes.
Fast forward
Keywords

Visual analytics for education, ChatGPT for education, student-ChatGPT interaction

Abstract

The integration of Large Language Models (LLMs), especially ChatGPT, into education is poised to revolutionize students’ learning experiences by introducing innovative conversational learning methodologies. To empower students to fully leverage the capabilities of ChatGPT in educational scenarios, understanding students’ interaction patterns with ChatGPT is crucial for instructors. However, this endeavor is challenging due to the absence of datasets focused on student-ChatGPT conversations and the complexities in identifying and analyzing the evolutional interaction patterns within conversations. To address these challenges, we collected conversational data from 48 students interacting with ChatGPT in a master’s level data visualization course over one semester. We then developed a coding scheme, grounded in the literature on cognitive levels and thematic analysis, to categorize students’ interaction patterns with ChatGPT. Furthermore, we present a visual analytics system, StuGPTViz, that tracks and compares temporal patterns in student prompts and the quality of ChatGPT’s responses at multiple scales, revealing significant pedagogical insights for instructors. We validated the system’s effectiveness through expert interviews with six data visualization instructors and three case studies. The results confirmed StuGPTViz’s capacity to enhance educators’ insights into the pedagogical value of ChatGPT. We also discussed the potential research opportunities of applying visual analytics in education and developing AI-driven personalized learning solutions.

IEEE VIS 2024 Content: VisEval: A Benchmark for Data Visualization in the Era of Large Language Models

Best Paper Award

VisEval: A Benchmark for Data Visualization in the Era of Large Language Models

Nan Chen - Microsoft Research, Shanghai, China

Yuge Zhang - Microsoft Research, Shanghai, China

Jiahang Xu - Microsoft Research, Shanghai, China

Kan Ren - ShanghaiTech University, Shanghai, China

Yuqing Yang - Microsoft Research, Shanghai, China

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Room: Bayshore I + II + III

2024-10-15T16:40:00Z GMT-0600 Change your timezone on the schedule page
2024-10-15T16:40:00Z
Exemplar figure, described by caption below
Examples of visualization issues detected by VisEval: Llama (CodeLlama-7B) produces code that cannot be executed, while Gemini (Gemini-Pro) incorrectly maps the "sum of Tonnage" to the y-axis instead of "count" and lacks a legend for the "Cargo ship" color. GPT-3.5 fails to sort as specified and places the legend outside the canvas. Although GPT-4 almost meets the requirements, it still encounters overflow issues that impact readability.
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Keywords

Visualization evaluation, automatic visualization, large language models, benchmark

Abstract

Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implementations, such as natural language processing and visualization design. Recent advancements in pre-trained large language models (LLMs) are opening new avenues for generating visualizations from natural language. However, the lack of a comprehensive and reliable benchmark hinders our understanding of LLMs’ capabilities in visualization generation. In this paper, we address this gap by proposing a new NL2VIS benchmark called VisEval. Firstly, we introduce a high-quality and large-scale dataset. This dataset includes 2,524 representative queries covering 146 databases, paired with accurately labeled ground truths. Secondly, we advocate for a comprehensive automated evaluation methodology covering multiple dimensions, including validity, legality, and readability. By systematically scanning for potential issues with a number of heterogeneous checkers, VisEval provides reliable and trustworthy evaluation outcomes. We run VisEval on a series of state-of-the-art LLMs. Our evaluation reveals prevalent challenges and delivers essential insights for future advancements.

IEEE VIS 2024 Content: VisEval: A Benchmark for Data Visualization in the Era of Large Language Models

Best Paper Award

VisEval: A Benchmark for Data Visualization in the Era of Large Language Models

Nan Chen - Microsoft Research, Shanghai, China

Yuge Zhang - Microsoft Research, Shanghai, China

Jiahang Xu - Microsoft Research, Shanghai, China

Kan Ren - ShanghaiTech University, Shanghai, China

Yuqing Yang - Microsoft Research, Shanghai, China

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Room: Bayshore I + II + III

2024-10-15T16:40:00Z GMT-0600 Change your timezone on the schedule page
2024-10-15T16:40:00Z
Exemplar figure, described by caption below
Examples of visualization issues detected by VisEval: Llama (CodeLlama-7B) produces code that cannot be executed, while Gemini (Gemini-Pro) incorrectly maps the "sum of Tonnage" to the y-axis instead of "count" and lacks a legend for the "Cargo ship" color. GPT-3.5 fails to sort as specified and places the legend outside the canvas. Although GPT-4 almost meets the requirements, it still encounters overflow issues that impact readability.
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Keywords

Visualization evaluation, automatic visualization, large language models, benchmark

Abstract

Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implementations, such as natural language processing and visualization design. Recent advancements in pre-trained large language models (LLMs) are opening new avenues for generating visualizations from natural language. However, the lack of a comprehensive and reliable benchmark hinders our understanding of LLMs’ capabilities in visualization generation. In this paper, we address this gap by proposing a new NL2VIS benchmark called VisEval. Firstly, we introduce a high-quality and large-scale dataset. This dataset includes 2,524 representative queries covering 146 databases, paired with accurately labeled ground truths. Secondly, we advocate for a comprehensive automated evaluation methodology covering multiple dimensions, including validity, legality, and readability. By systematically scanning for potential issues with a number of heterogeneous checkers, VisEval provides reliable and trustworthy evaluation outcomes. We run VisEval on a series of state-of-the-art LLMs. Our evaluation reveals prevalent challenges and delivers essential insights for future advancements.

IEEE VIS 2024 Content: Telling Data Stories with the Hero’s Journey: Design Guidance for Creating Data Videos

Telling Data Stories with the Hero’s Journey: Design Guidance for Creating Data Videos

Zheng Wei - The Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Huamin Qu - The Hong Kong University of Science and Technology, Hong Kong, China

Xian Xu - The Hong Kong University of Science and Technology, Hong Kong, China

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Room: Bayshore V

2024-10-17T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:12:00Z
Exemplar figure, described by caption below
Applying the Hero's Journey as a framework for creating data videos, we organize a design space into three segments (i.e., Departure, Initiation, Return), grounded in the narrative structure of the Hero's Journey. The Departure has six narrative stages, the Initiation has seven narrative stages, and the Return has four narrative stages. Each narrative stage is equipped with corresponding sound design and visual design.
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Keywords

The Hero's Journey, Narrative Structure, Narrative Visualization, Data Visualization, Data Videos

Abstract

Data videos increasingly becoming a popular data storytelling form represented by visual and audio integration. In recent years, more and more researchers have explored many narrative structures for effective and attractive data storytelling. Meanwhile, the Hero's Journey provides a classic narrative framework specific to the Hero's story that has been adopted by various mediums. There are continuous discussions about applying Hero's Journey to data stories. However, so far, little systematic and practical guidance on how to create a data video for a specific story type like the Hero's Journey, as well as how to manipulate its sound and visual designs simultaneously. To fulfill this gap, we first identified 48 data videos aligned with the Hero's Journey as the common storytelling from 109 high-quality data videos. Then, we examined how existing practices apply Hero's Journey for creating data videos. We coded the 48 data videos in terms of the narrative stages, sound design, and visual design according to the Hero's Journey structure. Based on our findings, we proposed a design space to provide practical guidance on the narrative, visual, and sound custom design for different narrative segments of the hero's journey (i.e., Departure, Initiation, Return) through data video creation. To validate our proposed design space, we conducted a user study where 20 participants were invited to design data videos with and without our design space guidance, which was evaluated by two experts. Results show that our design space provides useful and practical guidance for data storytellers effectively creating data videos with the Hero's Journey.

IEEE VIS 2024 Content: Telling Data Stories with the Hero’s Journey: Design Guidance for Creating Data Videos

Telling Data Stories with the Hero’s Journey: Design Guidance for Creating Data Videos

Zheng Wei - The Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Huamin Qu - The Hong Kong University of Science and Technology, Hong Kong, China

Xian Xu - The Hong Kong University of Science and Technology, Hong Kong, China

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Room: Bayshore V

2024-10-17T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:12:00Z
Exemplar figure, described by caption below
Applying the Hero's Journey as a framework for creating data videos, we organize a design space into three segments (i.e., Departure, Initiation, Return), grounded in the narrative structure of the Hero's Journey. The Departure has six narrative stages, the Initiation has seven narrative stages, and the Return has four narrative stages. Each narrative stage is equipped with corresponding sound design and visual design.
Fast forward
Keywords

The Hero's Journey, Narrative Structure, Narrative Visualization, Data Visualization, Data Videos

Abstract

Data videos increasingly becoming a popular data storytelling form represented by visual and audio integration. In recent years, more and more researchers have explored many narrative structures for effective and attractive data storytelling. Meanwhile, the Hero's Journey provides a classic narrative framework specific to the Hero's story that has been adopted by various mediums. There are continuous discussions about applying Hero's Journey to data stories. However, so far, little systematic and practical guidance on how to create a data video for a specific story type like the Hero's Journey, as well as how to manipulate its sound and visual designs simultaneously. To fulfill this gap, we first identified 48 data videos aligned with the Hero's Journey as the common storytelling from 109 high-quality data videos. Then, we examined how existing practices apply Hero's Journey for creating data videos. We coded the 48 data videos in terms of the narrative stages, sound design, and visual design according to the Hero's Journey structure. Based on our findings, we proposed a design space to provide practical guidance on the narrative, visual, and sound custom design for different narrative segments of the hero's journey (i.e., Departure, Initiation, Return) through data video creation. To validate our proposed design space, we conducted a user study where 20 participants were invited to design data videos with and without our design space guidance, which was evaluated by two experts. Results show that our design space provides useful and practical guidance for data storytellers effectively creating data videos with the Hero's Journey.

IEEE VIS 2024 Content: Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks

Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks

Astrid van den Brandt - Eindhoven University of Technology, Eindhoven, Netherlands

Sehi L'Yi - Harvard Medical School, Boston, United States

Huyen N. Nguyen - Harvard Medical School, Boston, United States

Anna Vilanova - Eindhoven University of Technology, Eindhoven, Netherlands

Nils Gehlenborg - Harvard Medical School, Boston, United States

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Room: Bayshore II

2024-10-17T17:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T17:00:00Z
Exemplar figure, described by caption below
Composite illustration summarizing key results from the two user studies. In Study 1 (n=20), we identified five personas based on interviews, characterized by three dimensions: focus, automation, and audience. In Study 2 (n=13), we collected user preferences across eight tasks (T1--T8) for six common authoring techniques: code-based, example-based, natural language input (NLI), shelf configuration, template-based, and visualization-by-demonstration (VbD).
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Keywords

User interviews, visual probes, visualization authoring, genomics data visualization

Abstract

Genomics experts rely on visualization to extract and share insights from complex and large-scale datasets. Beyond off-the-shelf tools for data exploration, there is an increasing need for platforms that aid experts in authoring customized visualizations for both exploration and communication of insights. A variety of interactive techniques have been proposed for authoring data visualizations, such as template editing, shelf configuration, natural language input, and code editors. However, it remains unclear how genomics experts create visualizations and which techniques best support their visualization tasks and needs. To address this gap, we conducted two user studies with genomics researchers: (1) semi-structured interviews (n=20) to identify the tasks, user contexts, and current visualization authoring techniques and (2) an exploratory study (n=13) using visual probes to elicit users’ intents and desired techniques when creating visualizations. Our contributions include (1) a characterization of how visualization authoring is currently utilized in genomics visualization, identifying limitations and benefits in light of common criteria for authoring tools, and (2) generalizable design implications for genomics visualization authoring tools based on our findings on task- and user-specific usefulness of authoring techniques. All supplemental materials are available at https://osf.io/bdj4v/.

IEEE VIS 2024 Content: Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks

Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks

Astrid van den Brandt - Eindhoven University of Technology, Eindhoven, Netherlands

Sehi L'Yi - Harvard Medical School, Boston, United States

Huyen N. Nguyen - Harvard Medical School, Boston, United States

Anna Vilanova - Eindhoven University of Technology, Eindhoven, Netherlands

Nils Gehlenborg - Harvard Medical School, Boston, United States

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Room: Bayshore II

2024-10-17T17:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T17:00:00Z
Exemplar figure, described by caption below
Composite illustration summarizing key results from the two user studies. In Study 1 (n=20), we identified five personas based on interviews, characterized by three dimensions: focus, automation, and audience. In Study 2 (n=13), we collected user preferences across eight tasks (T1--T8) for six common authoring techniques: code-based, example-based, natural language input (NLI), shelf configuration, template-based, and visualization-by-demonstration (VbD).
Fast forward
Keywords

User interviews, visual probes, visualization authoring, genomics data visualization

Abstract

Genomics experts rely on visualization to extract and share insights from complex and large-scale datasets. Beyond off-the-shelf tools for data exploration, there is an increasing need for platforms that aid experts in authoring customized visualizations for both exploration and communication of insights. A variety of interactive techniques have been proposed for authoring data visualizations, such as template editing, shelf configuration, natural language input, and code editors. However, it remains unclear how genomics experts create visualizations and which techniques best support their visualization tasks and needs. To address this gap, we conducted two user studies with genomics researchers: (1) semi-structured interviews (n=20) to identify the tasks, user contexts, and current visualization authoring techniques and (2) an exploratory study (n=13) using visual probes to elicit users’ intents and desired techniques when creating visualizations. Our contributions include (1) a characterization of how visualization authoring is currently utilized in genomics visualization, identifying limitations and benefits in light of common criteria for authoring tools, and (2) generalizable design implications for genomics visualization authoring tools based on our findings on task- and user-specific usefulness of authoring techniques. All supplemental materials are available at https://osf.io/bdj4v/.

IEEE VIS 2024 Content: Sportify: Question Answering with Embedded Visualizations and Personified Narratives for Sports Video

Sportify: Question Answering with Embedded Visualizations and Personified Narratives for Sports Video

Chunggi Lee - Harvard University, Allston, United States

Tica Lin - Harvard University, Cambridge, United States

Hanspeter Pfister - Harvard University, Cambridge, United States

Chen Zhu-Tian - University of Minnesota-Twin Cities, Minneapolis, United States

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Room: Bayshore V

2024-10-17T14:27:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T14:27:00Z
Exemplar figure, described by caption below
Sportify explains tactic questions in each clip for everyone, aiming to engage users and foster a love for sports. We integrate embedded visualization and personified narratives generated by large language model (LLM) to elucidate a complex series of actions through action detection, tactic classifier, and LLM pipelines.
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Keywords

Embedded Visualization, Narrative and storytelling, Basketball tactic, Question-answering (QA) system

Abstract

As basketball’s popularity surges, fans often find themselves confused and overwhelmed by the rapid game pace and complexity. Basketball tactics, involving a complex series of actions, require substantial knowledge to be fully understood. This complexity leads to a need for additional information and explanation, which can distract fans from the game. To tackle these challenges, we present Sportify, a Visual Question Answering system that integrates narratives and embedded visualization for demystifying basketball tactical questions, aiding fans in understanding various game aspects. We propose three novel action visualizations (i.e., Pass, Cut, and Screen) to demonstrate critical action sequences. To explain the reasoning and logic behind players’ actions, we leverage a large-language model (LLM) to generate narratives. We adopt a storytelling approach for complex scenarios from both first and third-person perspectives, integrating action visualizations. We evaluated Sportify with basketball fans to investigate its impact on understanding of tactics, and how different personal perspectives of narratives impact the understanding of complex tactic with action visualizations. Our evaluation with basketball fans demonstrates Sportify’s capability to deepen tactical insights and amplify the viewing experience. Furthermore, third-person narration assists people in getting in-depth game explanations while first-person narration enhances fans’ game engagement.

IEEE VIS 2024 Content: Sportify: Question Answering with Embedded Visualizations and Personified Narratives for Sports Video

Sportify: Question Answering with Embedded Visualizations and Personified Narratives for Sports Video

Chunggi Lee - Harvard University, Allston, United States

Tica Lin - Harvard University, Cambridge, United States

Hanspeter Pfister - Harvard University, Cambridge, United States

Chen Zhu-Tian - University of Minnesota-Twin Cities, Minneapolis, United States

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Room: Bayshore V

2024-10-17T14:27:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T14:27:00Z
Exemplar figure, described by caption below
Sportify explains tactic questions in each clip for everyone, aiming to engage users and foster a love for sports. We integrate embedded visualization and personified narratives generated by large language model (LLM) to elucidate a complex series of actions through action detection, tactic classifier, and LLM pipelines.
Fast forward
Keywords

Embedded Visualization, Narrative and storytelling, Basketball tactic, Question-answering (QA) system

Abstract

As basketball’s popularity surges, fans often find themselves confused and overwhelmed by the rapid game pace and complexity. Basketball tactics, involving a complex series of actions, require substantial knowledge to be fully understood. This complexity leads to a need for additional information and explanation, which can distract fans from the game. To tackle these challenges, we present Sportify, a Visual Question Answering system that integrates narratives and embedded visualization for demystifying basketball tactical questions, aiding fans in understanding various game aspects. We propose three novel action visualizations (i.e., Pass, Cut, and Screen) to demonstrate critical action sequences. To explain the reasoning and logic behind players’ actions, we leverage a large-language model (LLM) to generate narratives. We adopt a storytelling approach for complex scenarios from both first and third-person perspectives, integrating action visualizations. We evaluated Sportify with basketball fans to investigate its impact on understanding of tactics, and how different personal perspectives of narratives impact the understanding of complex tactic with action visualizations. Our evaluation with basketball fans demonstrates Sportify’s capability to deepen tactical insights and amplify the viewing experience. Furthermore, third-person narration assists people in getting in-depth game explanations while first-person narration enhances fans’ game engagement.

IEEE VIS 2024 Content: FPCS: Feature Preserving Compensated Sampling of Streaming Time Series Data

FPCS: Feature Preserving Compensated Sampling of Streaming Time Series Data

Hongyan Li - China Nanhu Academy of Electronics and Information Technology(CNAEIT), JiaXing, China

Bo Yang - China Nanhu Academy of Electronics and Information Technology(CNAEIT), JiaXing, China

Yansong Chua - China Nanhu Academy of Electronics and Information Technology, Jiaxing, China

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Room: Palma Ceia I

2024-10-16T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:30:00Z
Exemplar figure, described by caption below
The FPCS algorithm is used to sample streaming time series data. Each row corresponds to one of the five typical datasets. Columns 1-4 represent the visualization fitting effect of the first 100,000 data points in these datasets using the newly proposed FPCS and the other three algorithms, based on a 100:1 sampling ratio. The red line represents original data points; the green line represents sampled data points. Column 5 uses SSIM to compare the sampling effects of the four algorithms based on sampling ratios of 100:1, 200:1, 500:1, and 1000:1. The FPCS algorithm shows the best sampling results and performance.
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Keywords

Data visualization, Massive, Streaming, Time series, Line charts, Sampling, Feature, Compensating

Abstract

Data visualization aids in making data analysis more intuitive and in-depth, with widespread applications in fields such as biology, finance, and medicine. For massive and continuously growing streaming time series data, these data are typically visualized in the form of line charts, but the data transmission puts significant pressure on the network, leading to visualization lag or even failure to render completely. This paper proposes a universal sampling algorithm FPCS, which retains feature points from continuously received streaming time series data, compensates for the frequent fluctuating feature points, and aims to achieve efficient visualization. This algorithm bridges the gap in sampling for streaming time series data. The algorithm has several advantages: (1) It optimizes the sampling results by compensating for fewer feature points, retaining the visualization features of the original data very well, ensuring high-quality sampled data; (2) The execution time is the shortest compared to similar existing algorithms; (3) It has an almost negligible space overhead; (4) The data sampling process does not depend on the overall data; (5) This algorithm can be applied to infinite streaming data and finite static data.

IEEE VIS 2024 Content: FPCS: Feature Preserving Compensated Sampling of Streaming Time Series Data

FPCS: Feature Preserving Compensated Sampling of Streaming Time Series Data

Hongyan Li - China Nanhu Academy of Electronics and Information Technology(CNAEIT), JiaXing, China

Bo Yang - China Nanhu Academy of Electronics and Information Technology(CNAEIT), JiaXing, China

Yansong Chua - China Nanhu Academy of Electronics and Information Technology, Jiaxing, China

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Room: Palma Ceia I

2024-10-16T12:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:30:00Z
Exemplar figure, described by caption below
The FPCS algorithm is used to sample streaming time series data. Each row corresponds to one of the five typical datasets. Columns 1-4 represent the visualization fitting effect of the first 100,000 data points in these datasets using the newly proposed FPCS and the other three algorithms, based on a 100:1 sampling ratio. The red line represents original data points; the green line represents sampled data points. Column 5 uses SSIM to compare the sampling effects of the four algorithms based on sampling ratios of 100:1, 200:1, 500:1, and 1000:1. The FPCS algorithm shows the best sampling results and performance.
Fast forward
Keywords

Data visualization, Massive, Streaming, Time series, Line charts, Sampling, Feature, Compensating

Abstract

Data visualization aids in making data analysis more intuitive and in-depth, with widespread applications in fields such as biology, finance, and medicine. For massive and continuously growing streaming time series data, these data are typically visualized in the form of line charts, but the data transmission puts significant pressure on the network, leading to visualization lag or even failure to render completely. This paper proposes a universal sampling algorithm FPCS, which retains feature points from continuously received streaming time series data, compensates for the frequent fluctuating feature points, and aims to achieve efficient visualization. This algorithm bridges the gap in sampling for streaming time series data. The algorithm has several advantages: (1) It optimizes the sampling results by compensating for fewer feature points, retaining the visualization features of the original data very well, ensuring high-quality sampled data; (2) The execution time is the shortest compared to similar existing algorithms; (3) It has an almost negligible space overhead; (4) The data sampling process does not depend on the overall data; (5) This algorithm can be applied to infinite streaming data and finite static data.

IEEE VIS 2024 Content: SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction

Haoran Jiang - Shanghaitech University, Shanghai, China

Shaohan Shi - ShanghaiTech University, Shanghai, China

Shuhao Zhang - ShanghaiTech University, Shanghai, China

Jie Zheng - ShanghaiTech University, Shanghai, China

Quan Li - ShanghaiTech University, Shanghai, China

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Room: Bayshore V

2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:12:00Z
Exemplar figure, described by caption below
SLInterpreter, based on an iterative Human-AI collaboration framework, aims at 1) Human-Engaged Knowledge Graph Refinement based on Metapath Strategies and 2) Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis for domain experts. Domain experts explore new SL pairs using interpretive paths generated by a model trained on the entire data. Irrelevant or incorrect paths that may introduce noise are eliminated from the KG using appropriate metapath strategies. Subsequently, the model retrains, allowing domain experts to iteratively scrutinize predictions and interpretive paths, refining the KG. This iterative process optimizes predictions and mechanism exploration, enhancing expert participation and intervention, leading to increased trust.
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Keywords

Synthetic Lethality, Model Interpretability, Visual Analytics, Iterative Human-AI Collaboration.

Abstract

Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for interpretive paths and mechanism explorations that align better with domain-specific knowledge, particularly due to the high costs of experimentation. To address this gap, we propose an iterative Human-AI collaborative framework with two key components: 1) Human-Engaged Knowledge Graph Refinement based on Metapath Strategies, which leverages insights from interpretive paths and domain expertise to refine the knowledge graph through metapath strategies with appropriate granularity. 2) Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis, which aids experts in organizing and comparing predictions and interpretive paths across different granularities, uncovering new SL relationships, enhancing result interpretation, and elucidating potential mechanisms inferred by Graph Neural Network (GNN) models. These components cyclically optimize model predictions and mechanism explorations, enhancing expert involvement and intervention to build trust. Facilitated by SLInterpreter, this framework ensures that newly generated interpretive paths increasingly align with domain knowledge and adhere more closely to real-world biological principles through iterative Human-AI collaboration. We evaluate the framework’s efficacy through a case study and expert interviews.

IEEE VIS 2024 Content: SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction

Haoran Jiang - Shanghaitech University, Shanghai, China

Shaohan Shi - ShanghaiTech University, Shanghai, China

Shuhao Zhang - ShanghaiTech University, Shanghai, China

Jie Zheng - ShanghaiTech University, Shanghai, China

Quan Li - ShanghaiTech University, Shanghai, China

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Room: Bayshore V

2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:12:00Z
Exemplar figure, described by caption below
SLInterpreter, based on an iterative Human-AI collaboration framework, aims at 1) Human-Engaged Knowledge Graph Refinement based on Metapath Strategies and 2) Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis for domain experts. Domain experts explore new SL pairs using interpretive paths generated by a model trained on the entire data. Irrelevant or incorrect paths that may introduce noise are eliminated from the KG using appropriate metapath strategies. Subsequently, the model retrains, allowing domain experts to iteratively scrutinize predictions and interpretive paths, refining the KG. This iterative process optimizes predictions and mechanism exploration, enhancing expert participation and intervention, leading to increased trust.
Fast forward
Keywords

Synthetic Lethality, Model Interpretability, Visual Analytics, Iterative Human-AI Collaboration.

Abstract

Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for interpretive paths and mechanism explorations that align better with domain-specific knowledge, particularly due to the high costs of experimentation. To address this gap, we propose an iterative Human-AI collaborative framework with two key components: 1) Human-Engaged Knowledge Graph Refinement based on Metapath Strategies, which leverages insights from interpretive paths and domain expertise to refine the knowledge graph through metapath strategies with appropriate granularity. 2) Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis, which aids experts in organizing and comparing predictions and interpretive paths across different granularities, uncovering new SL relationships, enhancing result interpretation, and elucidating potential mechanisms inferred by Graph Neural Network (GNN) models. These components cyclically optimize model predictions and mechanism explorations, enhancing expert involvement and intervention to build trust. Facilitated by SLInterpreter, this framework ensures that newly generated interpretive paths increasingly align with domain knowledge and adhere more closely to real-world biological principles through iterative Human-AI collaboration. We evaluate the framework’s efficacy through a case study and expert interviews.

IEEE VIS 2024 Content: StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization

StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization

Kaiyuan Tang - University of Notre Dame, Notre Dame, United States

Chaoli Wang - University of Notre Dame, Notre Dame, United States

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Room: Bayshore I

2024-10-16T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:54:00Z
Exemplar figure, described by caption below
StyleRF-VolVis is an innovative style transfer framework based on the neural radiance field for expressive volume visualization. This framework contains three components: a base NeRF model for ensuring accurate geometry reconstruction, a palette color network to support photorealistic style editing, and an unrestricted color network to achieve non-photorealistic style editing.
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Keywords

Style transfer, neural radiance field, knowledge distillation, volume visualization

Abstract

In volume visualization, visualization synthesis has attracted much attention due to its ability to generate novel visualizations without following the conventional rendering pipeline. However, existing solutions based on generative adversarial networks often require many training images and take significant training time. Still, issues such as low quality, consistency, and flexibility persist. This paper introduces StyleRF-VolVis, an innovative style transfer framework for expressive volume visualization (VolVis) via neural radiance field (NeRF). The expressiveness of StyleRF-VolVis is upheld by its ability to accurately separate the underlying scene geometry (i.e., content) and color appearance (i.e., style), conveniently modify color, opacity, and lighting of the original rendering while maintaining visual content consistency across the views, and effectively transfer arbitrary styles from reference images to the reconstructed 3D scene. To achieve these, we design a base NeRF model for scene geometry extraction, a palette color network to classify regions of the radiance field for photorealistic editing, and an unrestricted color network to lift the color palette constraint via knowledge distillation for non-photorealistic editing. We demonstrate the superior quality, consistency, and flexibility of StyleRF-VolVis by experimenting with various volume rendering scenes and reference images and comparing StyleRF-VolVis against other image-based (AdaIN), video-based (ReReVST), and NeRF-based (ARF and SNeRF) style rendering solutions.

IEEE VIS 2024 Content: StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization

StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization

Kaiyuan Tang - University of Notre Dame, Notre Dame, United States

Chaoli Wang - University of Notre Dame, Notre Dame, United States

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Room: Bayshore I

2024-10-16T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T12:54:00Z
Exemplar figure, described by caption below
StyleRF-VolVis is an innovative style transfer framework based on the neural radiance field for expressive volume visualization. This framework contains three components: a base NeRF model for ensuring accurate geometry reconstruction, a palette color network to support photorealistic style editing, and an unrestricted color network to achieve non-photorealistic style editing.
Fast forward
Keywords

Style transfer, neural radiance field, knowledge distillation, volume visualization

Abstract

In volume visualization, visualization synthesis has attracted much attention due to its ability to generate novel visualizations without following the conventional rendering pipeline. However, existing solutions based on generative adversarial networks often require many training images and take significant training time. Still, issues such as low quality, consistency, and flexibility persist. This paper introduces StyleRF-VolVis, an innovative style transfer framework for expressive volume visualization (VolVis) via neural radiance field (NeRF). The expressiveness of StyleRF-VolVis is upheld by its ability to accurately separate the underlying scene geometry (i.e., content) and color appearance (i.e., style), conveniently modify color, opacity, and lighting of the original rendering while maintaining visual content consistency across the views, and effectively transfer arbitrary styles from reference images to the reconstructed 3D scene. To achieve these, we design a base NeRF model for scene geometry extraction, a palette color network to classify regions of the radiance field for photorealistic editing, and an unrestricted color network to lift the color palette constraint via knowledge distillation for non-photorealistic editing. We demonstrate the superior quality, consistency, and flexibility of StyleRF-VolVis by experimenting with various volume rendering scenes and reference images and comparing StyleRF-VolVis against other image-based (AdaIN), video-based (ReReVST), and NeRF-based (ARF and SNeRF) style rendering solutions.

IEEE VIS 2024 Content: Practices and Strategies in Responsive Thematic Map Design: A Report from Design Workshops with Experts

Practices and Strategies in Responsive Thematic Map Design: A Report from Design Workshops with Experts

Sarah Schöttler - University of Edinburgh, Edinburgh, United Kingdom

Uta Hinrichs - University of Edinburgh, Edinburgh, United Kingdom

Benjamin Bach - Inria, Bordeaux, France. University of Edinburgh, Edinburgh, United Kingdom

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Room: Bayshore II

2024-10-17T16:24:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:24:00Z
Exemplar figure, described by caption below
Challenges and design solutions for responsive thematic mapping. On the left, seven common challenges in responsive thematic maps, such as areas and symbols being too small or overlapping, are displayed. On the right, 17 possible design solutions are displayed, for example replacing the legend with annotations, separating the map into segments, or scrolling the map.
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Keywords

information visualization, responsive visualization, thematic map design

Abstract

This paper discusses challenges and design strategies in responsive design for thematic maps in information visualization. Thematic maps pose a number of unique challenges for responsiveness, such as inflexible aspect ratios that do not easily adapt to varying screen dimensions, or densely clustered visual elements in urban areas becoming illegible at smaller scales. However, design guidance on how to best address these issues is currently lacking. We conducted design sessions with eight professional designers and developers of web-based thematic maps for information visualization. Participants were asked to redesign a given map for various screen sizes and aspect ratios and to describe their reasoning for when and how they adapted the design. We report general observations of practitioners’ motivations, decision-making processes, and personal design frameworks. We then derive seven challenges commonly encountered in responsive maps, and 17 strategies to address them, such as repositioning elements, segmenting the map, or using alternative visualizations. We compile these challenges and strategies into an illustrated cheat sheet targeted at anyone designing or learning to design responsive maps. The cheat sheet is available online: responsive-vis.github.io/map-cheat-sheet.

IEEE VIS 2024 Content: Practices and Strategies in Responsive Thematic Map Design: A Report from Design Workshops with Experts

Practices and Strategies in Responsive Thematic Map Design: A Report from Design Workshops with Experts

Sarah Schöttler - University of Edinburgh, Edinburgh, United Kingdom

Uta Hinrichs - University of Edinburgh, Edinburgh, United Kingdom

Benjamin Bach - Inria, Bordeaux, France. University of Edinburgh, Edinburgh, United Kingdom

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Room: Bayshore II

2024-10-17T16:24:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:24:00Z
Exemplar figure, described by caption below
Challenges and design solutions for responsive thematic mapping. On the left, seven common challenges in responsive thematic maps, such as areas and symbols being too small or overlapping, are displayed. On the right, 17 possible design solutions are displayed, for example replacing the legend with annotations, separating the map into segments, or scrolling the map.
Fast forward
Keywords

information visualization, responsive visualization, thematic map design

Abstract

This paper discusses challenges and design strategies in responsive design for thematic maps in information visualization. Thematic maps pose a number of unique challenges for responsiveness, such as inflexible aspect ratios that do not easily adapt to varying screen dimensions, or densely clustered visual elements in urban areas becoming illegible at smaller scales. However, design guidance on how to best address these issues is currently lacking. We conducted design sessions with eight professional designers and developers of web-based thematic maps for information visualization. Participants were asked to redesign a given map for various screen sizes and aspect ratios and to describe their reasoning for when and how they adapted the design. We report general observations of practitioners’ motivations, decision-making processes, and personal design frameworks. We then derive seven challenges commonly encountered in responsive maps, and 17 strategies to address them, such as repositioning elements, segmenting the map, or using alternative visualizations. We compile these challenges and strategies into an illustrated cheat sheet targeted at anyone designing or learning to design responsive maps. The cheat sheet is available online: responsive-vis.github.io/map-cheat-sheet.

IEEE VIS 2024 Content: Discursive Patinas: Anchoring Discussions in Data Visualizations

Discursive Patinas: Anchoring Discussions in Data Visualizations

Tobias Kauer - University of Edinburgh, Edinburgh, United Kingdom. Potsdam University of Applied Sciences, Potsdam, Germany

Derya Akbaba - Linköping University, Norrköping, Sweden

Marian Dörk - University of Applied Sciences Potsdam, Potsdam, Germany

Benjamin Bach - Inria, Bordeaux, France. University of Edinburgh, Edinburgh, United Kingdom

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Room: Bayshore II

2024-10-17T14:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T14:15:00Z
Exemplar figure, described by caption below
Discursive Patinas present a new technique that visualizes discussions about visualizations, inspired by traces left in the physical world
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Keywords

Data Visualization, Discussion, Annotation

Abstract

This paper presents discursive patinas, a technique to visualize discussions onto data visualizations, inspired by how people leave traces in the physical world. While data visualizations are widely discussed in online communities and social media, comments tend to be displayed separately from the visualization and we lack ways to relate these discussions back to the content of the visualization, e.g., to situate comments, explain visual patterns, or question assumptions. In our visualization annotation interface, users can designate areas within the visualization. Discursive patinas are made of overlaid visual marks (anchors), attached to textual comments with category labels, likes, and replies. By coloring and styling the anchors, a meta visualization emerges, showing what and where people comment and annotate the visualization. These patinas show regions of heavy discussions, recent commenting activity, and the distribution of questions, suggestions, or personal stories. We ran workshops with 90 students, domain experts, and visualization researchers to study how people use anchors to discuss visualizations and how patinas influence people's understanding of the discussion. Our results show that discursive patinas improve the ability to navigate discussions and guide people to comments that help understand, contextualize, or scrutinize the visualization. We discuss the potential of anchors and patinas to support discursive engagements, including critical readings of visualizations, design feedback, and feminist approaches to data visualization.

IEEE VIS 2024 Content: Discursive Patinas: Anchoring Discussions in Data Visualizations

Discursive Patinas: Anchoring Discussions in Data Visualizations

Tobias Kauer - University of Edinburgh, Edinburgh, United Kingdom. Potsdam University of Applied Sciences, Potsdam, Germany

Derya Akbaba - Linköping University, Norrköping, Sweden

Marian Dörk - University of Applied Sciences Potsdam, Potsdam, Germany

Benjamin Bach - Inria, Bordeaux, France. University of Edinburgh, Edinburgh, United Kingdom

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Room: Bayshore II

2024-10-17T14:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T14:15:00Z
Exemplar figure, described by caption below
Discursive Patinas present a new technique that visualizes discussions about visualizations, inspired by traces left in the physical world
Fast forward
Keywords

Data Visualization, Discussion, Annotation

Abstract

This paper presents discursive patinas, a technique to visualize discussions onto data visualizations, inspired by how people leave traces in the physical world. While data visualizations are widely discussed in online communities and social media, comments tend to be displayed separately from the visualization and we lack ways to relate these discussions back to the content of the visualization, e.g., to situate comments, explain visual patterns, or question assumptions. In our visualization annotation interface, users can designate areas within the visualization. Discursive patinas are made of overlaid visual marks (anchors), attached to textual comments with category labels, likes, and replies. By coloring and styling the anchors, a meta visualization emerges, showing what and where people comment and annotate the visualization. These patinas show regions of heavy discussions, recent commenting activity, and the distribution of questions, suggestions, or personal stories. We ran workshops with 90 students, domain experts, and visualization researchers to study how people use anchors to discuss visualizations and how patinas influence people's understanding of the discussion. Our results show that discursive patinas improve the ability to navigate discussions and guide people to comments that help understand, contextualize, or scrutinize the visualization. We discuss the potential of anchors and patinas to support discursive engagements, including critical readings of visualizations, design feedback, and feminist approaches to data visualization.

IEEE VIS 2024 Content: D-Tour: Semi-Automatic Generation of Interactive Guided Tours for Visualization Dashboard Onboarding

D-Tour: Semi-Automatic Generation of Interactive Guided Tours for Visualization Dashboard Onboarding

Vaishali Dhanoa - Pro2Future GmbH, Linz, Austria. Johannes Kepler University, Linz, Austria

Andreas Hinterreiter - Johannes Kepler University, Linz, Austria

Vanessa Fediuk - Johannes Kepler University, Linz, Austria

Niklas Elmqvist - Aarhus University, Aarhus, Denmark

Eduard Gröller - Institute of Visual Computing . Human-Centered Technology, Vienna, Austria

Marc Streit - Johannes Kepler University Linz, Linz, Austria

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Room: Bayshore II

2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:18:00Z
Exemplar figure, described by caption below
D-Tour Prototype Authoring Mode. Authors pick (a) automatically extracted visualization categories, General, Insight, or Interaction from the Content Extraction View and drag them to the Content Arrangement View, where they (b) arrange them, (b.1) thus crafting a tour and (b.2) adding explanations to the tour content. In the Dissemination View they (c) test changes before disseminating them. A selection of the Column Chart General in the Content Extraction View is shown which is highlighted in the Content Arrangement View and in the Dissemination View. Its associated content can be seen in (b.2)
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Keywords

Dashboards, onboarding, storytelling, tutorial, interactive tours, open-world games

Abstract

Onboarding a user to a visualization dashboard entails explaining its various components, including the chart types used, the data loaded, and the interactions available. Authoring such an onboarding experience is time-consuming and requires significant knowledge and little guidance on how best to complete this task. Depending on their levels of expertise, end users being onboarded to a new dashboard can be either confused and overwhelmed or disinterested and disengaged. We propose interactive dashboard tours (D-Tours) as semi-automated onboarding experiences that preserve the agency of users with various levels of expertise to keep them interested and engaged. Our interactive tours concept draws from open-world game design to give the user freedom in choosing their path through onboarding. We have implemented the concept in a tool called D-TOUR PROTOTYPE, which allows authors to craft custom interactive dashboard tours from scratch or using automatic templates. Automatically generated tours can still be customized to use different media (e.g., video, audio, and highlighting) or new narratives to produce an onboarding experience tailored to an individual user. We demonstrate the usefulness of interactive dashboard tours through use cases and expert interviews. Our evaluation shows that authors found the automation in the D-Tour Prototype helpful and time-saving, and users found the created tours engaging and intuitive. This paper and all supplemental materials are available at https://osf.io/6fbjp/.

IEEE VIS 2024 Content: D-Tour: Semi-Automatic Generation of Interactive Guided Tours for Visualization Dashboard Onboarding

D-Tour: Semi-Automatic Generation of Interactive Guided Tours for Visualization Dashboard Onboarding

Vaishali Dhanoa - Pro2Future GmbH, Linz, Austria. Johannes Kepler University, Linz, Austria

Andreas Hinterreiter - Johannes Kepler University, Linz, Austria

Vanessa Fediuk - Johannes Kepler University, Linz, Austria

Niklas Elmqvist - Aarhus University, Aarhus, Denmark

Eduard Gröller - Institute of Visual Computing . Human-Centered Technology, Vienna, Austria

Marc Streit - Johannes Kepler University Linz, Linz, Austria

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Room: Bayshore II

2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:18:00Z
Exemplar figure, described by caption below
D-Tour Prototype Authoring Mode. Authors pick (a) automatically extracted visualization categories, General, Insight, or Interaction from the Content Extraction View and drag them to the Content Arrangement View, where they (b) arrange them, (b.1) thus crafting a tour and (b.2) adding explanations to the tour content. In the Dissemination View they (c) test changes before disseminating them. A selection of the Column Chart General in the Content Extraction View is shown which is highlighted in the Content Arrangement View and in the Dissemination View. Its associated content can be seen in (b.2)
Fast forward
Keywords

Dashboards, onboarding, storytelling, tutorial, interactive tours, open-world games

Abstract

Onboarding a user to a visualization dashboard entails explaining its various components, including the chart types used, the data loaded, and the interactions available. Authoring such an onboarding experience is time-consuming and requires significant knowledge and little guidance on how best to complete this task. Depending on their levels of expertise, end users being onboarded to a new dashboard can be either confused and overwhelmed or disinterested and disengaged. We propose interactive dashboard tours (D-Tours) as semi-automated onboarding experiences that preserve the agency of users with various levels of expertise to keep them interested and engaged. Our interactive tours concept draws from open-world game design to give the user freedom in choosing their path through onboarding. We have implemented the concept in a tool called D-TOUR PROTOTYPE, which allows authors to craft custom interactive dashboard tours from scratch or using automatic templates. Automatically generated tours can still be customized to use different media (e.g., video, audio, and highlighting) or new narratives to produce an onboarding experience tailored to an individual user. We demonstrate the usefulness of interactive dashboard tours through use cases and expert interviews. Our evaluation shows that authors found the automation in the D-Tour Prototype helpful and time-saving, and users found the created tours engaging and intuitive. This paper and all supplemental materials are available at https://osf.io/6fbjp/.

IEEE VIS 2024 Content: Unveiling How Examples Shape Data Visualization Design Outcomes

Unveiling How Examples Shape Data Visualization Design Outcomes

Hannah K. Bako - University of Maryland, College Park, United States

Xinyi Liu - The University of Texas at Austin, Austin, United States

Grace Ko - University of Maryland, College Park, United States

Hyemi Song - Human Data Interaction Lab, College Park, United States

Leilani Battle - University of Washington, Seattle, United States

Zhicheng Liu - University of Maryland, College Park, United States

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Room: Bayshore II

2024-10-17T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:12:00Z
Exemplar figure, described by caption below
The image outlines an exploratory study investigating how the timing and properties of examples influence visualization design outcomes, highlighting key stages from task introduction to final design selection.
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Keywords

data visualization, design, examples

Abstract

Visualization designers (e.g., journalists or data analysts) often rely on examples to explore the space of possible designs, yet we have little insight into how examples shape data visualization design outcomes. While the effects of examples have been studied in other disciplines, such as web design or engineering, the results are not readily applicable to visualization due to inconsistencies in findings and challenges unique to visualization design. Towards bridging this gap, we conduct an exploratory experiment involving 32 data visualization designers focusing on the influence of five factors (timing, quantity, diversity, data topic similarity, and data schema similarity) on objectively measurable design outcomes (e.g., numbers of designs and idea transfers). Our quantitative analysis shows that when examples are introduced after initial brainstorming, designers curate examples with topics less similar to the dataset they are working on and produce more designs with a high variation in visualization components. Also, designers copy more ideas from examples with higher data schema similarities. Our qualitative analysis of participants’ thought processes provides insights into why designers incorporate examples into their designs, revealing potential factors that have not been previously investigated. Finally, we discuss how our results inform how designers may use examples during design ideation as well as future research on quantifying designs and supporting example-based visualization design. All supplemental materials are available in our OSF repo.

IEEE VIS 2024 Content: Unveiling How Examples Shape Data Visualization Design Outcomes

Unveiling How Examples Shape Data Visualization Design Outcomes

Hannah K. Bako - University of Maryland, College Park, United States

Xinyi Liu - The University of Texas at Austin, Austin, United States

Grace Ko - University of Maryland, College Park, United States

Hyemi Song - Human Data Interaction Lab, College Park, United States

Leilani Battle - University of Washington, Seattle, United States

Zhicheng Liu - University of Maryland, College Park, United States

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Room: Bayshore II

2024-10-17T16:12:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T16:12:00Z
Exemplar figure, described by caption below
The image outlines an exploratory study investigating how the timing and properties of examples influence visualization design outcomes, highlighting key stages from task introduction to final design selection.
Fast forward
Keywords

data visualization, design, examples

Abstract

Visualization designers (e.g., journalists or data analysts) often rely on examples to explore the space of possible designs, yet we have little insight into how examples shape data visualization design outcomes. While the effects of examples have been studied in other disciplines, such as web design or engineering, the results are not readily applicable to visualization due to inconsistencies in findings and challenges unique to visualization design. Towards bridging this gap, we conduct an exploratory experiment involving 32 data visualization designers focusing on the influence of five factors (timing, quantity, diversity, data topic similarity, and data schema similarity) on objectively measurable design outcomes (e.g., numbers of designs and idea transfers). Our quantitative analysis shows that when examples are introduced after initial brainstorming, designers curate examples with topics less similar to the dataset they are working on and produce more designs with a high variation in visualization components. Also, designers copy more ideas from examples with higher data schema similarities. Our qualitative analysis of participants’ thought processes provides insights into why designers incorporate examples into their designs, revealing potential factors that have not been previously investigated. Finally, we discuss how our results inform how designers may use examples during design ideation as well as future research on quantifying designs and supporting example-based visualization design. All supplemental materials are available in our OSF repo.

IEEE VIS 2024 Content: Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes

Honorable Mention

Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes

Zhicheng Liu - University of Maryland, College Park, United States

Chen Chen - University of Maryland, College Park, United States

John Hooker - University of Maryland, College Park, United States

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Room: Bayshore II

2024-10-17T13:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:30:00Z
Exemplar figure, described by caption below
We present Manipulable Semantic Components (MSC), a computational representation of data visualization scenes. MSC consists of two parts: a unified object model describing the structure of a visualization scene, and a set of operations to generate and modify the scene components. We demonstrate the benefits of MSC in three case studies.
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Keywords

data visualization, scene abstraction, visualization model

Abstract

Various data visualization applications such as reverse engineering and interactive authoring require a vocabulary that describes the structure of visualization scenes and the procedure to manipulate them. A few scene abstractions have been proposed, but they are restricted to specific applications for a limited set of visualization types. A unified and expressive model of data visualization scenes for different applications has been missing. To fill this gap, we present Manipulable Semantic Components (MSC), a computational representation of data visualization scenes, to support applications in scene understanding and augmentation. MSC consists of two parts: a unified object model describing the structure of a visualization scene in terms of semantic components, and a set of operations to generate and modify the scene components. We demonstrate the benefits of MSC in three applications: visualization authoring, visualization deconstruction and reuse, and animation specification.

IEEE VIS 2024 Content: Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes

Honorable Mention

Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes

Zhicheng Liu - University of Maryland, College Park, United States

Chen Chen - University of Maryland, College Park, United States

John Hooker - University of Maryland, College Park, United States

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Room: Bayshore II

2024-10-17T13:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T13:30:00Z
Exemplar figure, described by caption below
We present Manipulable Semantic Components (MSC), a computational representation of data visualization scenes. MSC consists of two parts: a unified object model describing the structure of a visualization scene, and a set of operations to generate and modify the scene components. We demonstrate the benefits of MSC in three case studies.
Fast forward
Keywords

data visualization, scene abstraction, visualization model

Abstract

Various data visualization applications such as reverse engineering and interactive authoring require a vocabulary that describes the structure of visualization scenes and the procedure to manipulate them. A few scene abstractions have been proposed, but they are restricted to specific applications for a limited set of visualization types. A unified and expressive model of data visualization scenes for different applications has been missing. To fill this gap, we present Manipulable Semantic Components (MSC), a computational representation of data visualization scenes, to support applications in scene understanding and augmentation. MSC consists of two parts: a unified object model describing the structure of a visualization scene in terms of semantic components, and a set of operations to generate and modify the scene components. We demonstrate the benefits of MSC in three applications: visualization authoring, visualization deconstruction and reuse, and animation specification.

IEEE VIS 2024 Content: Promises and Pitfalls: Using Large Language Models to Generate Visualization Items

Promises and Pitfalls: Using Large Language Models to Generate Visualization Items

Yuan Cui - Northwestern University, Evanston, United States

Lily W. Ge - Northwestern University, Evanston, United States

Yiren Ding - Worcester Polytechnic Institute, Worcester, United States

Lane Harrison - Worcester Polytechnic Institute, Worcester, United States

Fumeng Yang - Northwestern University, Evanston, United States

Matthew Kay - Northwestern University, Chicago, United States

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Room: Bayshore I + II + III

2024-10-18T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:06:00Z
Exemplar figure, described by caption below
Overview of this paper: developing the VILA pipeline, evaluating the candidate bank, and demonstrating a potential application— the new VILA-VLAT visualization literacy test.
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Keywords

Visualization Items, Large Language Models, Visualization Literacy Assessment

Abstract

Visualization items—factual questions about visualizations that ask viewers to accomplish visualization tasks—are regularly used in the field of information visualization as educational and evaluative materials. For example, researchers of visualization literacy require large, diverse banks of items to conduct studies where the same skill is measured repeatedly on the same participants. Yet, generating a large number of high-quality, diverse items requires significant time and expertise. To address the critical need for a large number of diverse visualization items in education and research, this paper investigates the potential for large language models (LLMs) to automate the generation of multiple-choice visualization items. Through an iterative design process, we develop the VILA (Visualization Items Generated by Large LAnguage Models) pipeline, for efficiently generating visualization items that measure people’s ability to accomplish visualization tasks. We use the VILA pipeline to generate 1,404 candidate items across 12 chart types and 13 visualization tasks. In collaboration with 11 visualization experts, we develop an evaluation rulebook which we then use to rate the quality of all candidate items. The result is the VILA bank of ∼1,100 items. From this evaluation, we also identify and classify current limitations of the VILA pipeline, and discuss the role of human oversight in ensuring quality. In addition, we demonstrate an application of our work by creating a visualization literacy test, VILA-VLAT, which measures people’s ability to complete a diverse set of tasks on various types of visualizations; comparing it to the existing VLAT, VILA-VLAT shows moderate to high convergent validity (R = 0.70). Lastly, we discuss the application areas of the VILA pipeline and the VILA bank and provide practical recommendations for their use. All supplemental materials are available at https://osf.io/ysrhq/.

IEEE VIS 2024 Content: Promises and Pitfalls: Using Large Language Models to Generate Visualization Items

Promises and Pitfalls: Using Large Language Models to Generate Visualization Items

Yuan Cui - Northwestern University, Evanston, United States

Lily W. Ge - Northwestern University, Evanston, United States

Yiren Ding - Worcester Polytechnic Institute, Worcester, United States

Lane Harrison - Worcester Polytechnic Institute, Worcester, United States

Fumeng Yang - Northwestern University, Evanston, United States

Matthew Kay - Northwestern University, Chicago, United States

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Room: Bayshore I + II + III

2024-10-18T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T13:06:00Z
Exemplar figure, described by caption below
Overview of this paper: developing the VILA pipeline, evaluating the candidate bank, and demonstrating a potential application— the new VILA-VLAT visualization literacy test.
Fast forward
Keywords

Visualization Items, Large Language Models, Visualization Literacy Assessment

Abstract

Visualization items—factual questions about visualizations that ask viewers to accomplish visualization tasks—are regularly used in the field of information visualization as educational and evaluative materials. For example, researchers of visualization literacy require large, diverse banks of items to conduct studies where the same skill is measured repeatedly on the same participants. Yet, generating a large number of high-quality, diverse items requires significant time and expertise. To address the critical need for a large number of diverse visualization items in education and research, this paper investigates the potential for large language models (LLMs) to automate the generation of multiple-choice visualization items. Through an iterative design process, we develop the VILA (Visualization Items Generated by Large LAnguage Models) pipeline, for efficiently generating visualization items that measure people’s ability to accomplish visualization tasks. We use the VILA pipeline to generate 1,404 candidate items across 12 chart types and 13 visualization tasks. In collaboration with 11 visualization experts, we develop an evaluation rulebook which we then use to rate the quality of all candidate items. The result is the VILA bank of ∼1,100 items. From this evaluation, we also identify and classify current limitations of the VILA pipeline, and discuss the role of human oversight in ensuring quality. In addition, we demonstrate an application of our work by creating a visualization literacy test, VILA-VLAT, which measures people’s ability to complete a diverse set of tasks on various types of visualizations; comparing it to the existing VLAT, VILA-VLAT shows moderate to high convergent validity (R = 0.70). Lastly, we discuss the application areas of the VILA pipeline and the VILA bank and provide practical recommendations for their use. All supplemental materials are available at https://osf.io/ysrhq/.

IEEE VIS 2024 Content: DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs

DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs

Joohee Kim - Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of

Hyunwook Lee - Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of

Duc M. Nguyen - Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of

Minjeong Shin - Australian National University, Canberra, Australia

Bum Chul Kwon - IBM Research, Cambridge, United States

Sungahn Ko - UNIST, Ulsan, Korea, Republic of

Niklas Elmqvist - Aarhus University, Aarhus, Denmark

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Room: Bayshore V

2024-10-17T17:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T17:00:00Z
Exemplar figure, described by caption below
DG Comics offers a Summary View that facilitates the automatic generation of comic templates, sliders for filtering and highlighting nodes, a Graph Comic View for editing the graph comic, and Main Character and Supporting Character tables for managing nodes. It also includes a Timeline View for exploring graph snapshots. Users can switch to the Node Attribute Table to select specific main characters or to the Community View to inspect the evolution of node relationships. The tool supports (M) mental map preservation by fixing nodes across displays and visualizes (O) community changes using bubble sets.
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Keywords

Data-driven storytelling, narrative visualization, dynamic graphs, graph comics

Abstract

Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs—graphs whose vertices and edges change over time. However, manually creating such comics is currently time-consuming, complex, and error-prone. In this paper, we propose DG Comics, a novel comic authoring tool for dynamic graphs that allows users to semi-automatically build and annotate comics. The tool uses a newly developed hierarchical clustering algorithm to segment consecutive snapshots of dynamic graphs while preserving their chronological order. It also presents rich information on both individuals and communities extracted from dynamic graphs in multiple views, where users can explore dynamic graphs and choose what to tell in comics. For evaluation, we provide an example and report the results of a user study and an expert review.

IEEE VIS 2024 Content: DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs

DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs

Joohee Kim - Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of

Hyunwook Lee - Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of

Duc M. Nguyen - Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of

Minjeong Shin - Australian National University, Canberra, Australia

Bum Chul Kwon - IBM Research, Cambridge, United States

Sungahn Ko - UNIST, Ulsan, Korea, Republic of

Niklas Elmqvist - Aarhus University, Aarhus, Denmark

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Room: Bayshore V

2024-10-17T17:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T17:00:00Z
Exemplar figure, described by caption below
DG Comics offers a Summary View that facilitates the automatic generation of comic templates, sliders for filtering and highlighting nodes, a Graph Comic View for editing the graph comic, and Main Character and Supporting Character tables for managing nodes. It also includes a Timeline View for exploring graph snapshots. Users can switch to the Node Attribute Table to select specific main characters or to the Community View to inspect the evolution of node relationships. The tool supports (M) mental map preservation by fixing nodes across displays and visualizes (O) community changes using bubble sets.
Fast forward
Keywords

Data-driven storytelling, narrative visualization, dynamic graphs, graph comics

Abstract

Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs—graphs whose vertices and edges change over time. However, manually creating such comics is currently time-consuming, complex, and error-prone. In this paper, we propose DG Comics, a novel comic authoring tool for dynamic graphs that allows users to semi-automatically build and annotate comics. The tool uses a newly developed hierarchical clustering algorithm to segment consecutive snapshots of dynamic graphs while preserving their chronological order. It also presents rich information on both individuals and communities extracted from dynamic graphs in multiple views, where users can explore dynamic graphs and choose what to tell in comics. For evaluation, we provide an example and report the results of a user study and an expert review.

IEEE VIS 2024 Content: ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging

ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging

Guan Li - Computer Network Information Center, Chinese Academy of Sciences

Yang Liu - Beijing Forestry University

Guihua Shan - Computer Network Information Center, Chinese Academy of Sciences

Shiyu Cheng - Chinese Academy of Sciences

Weiqun Cao - Beijing Forestry University

Junpeng Wang - Visa Research

Ko-Chih Wang - National Taiwan Normal University

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Room: Bayshore I

2024-10-16T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T13:06:00Z
Exemplar figure, described by caption below
ParamsDrag is a surrogate model developed to enhance the exploration of parameter spaces through direct interaction with visualizations. It allows scientists to intuitively manipulate a feature of interest by dragging it to a desired location within a visualization, subsequently generating the corresponding image. Additionally, ParamsDrag can retrieve the simulation parameters that led to the generation of the selected image, thereby streamlining the process of parameter identification and adjustment.
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Keywords

parameter exploration, feature interaction, parameter inversion

Abstract

Numerical simulation serves as a cornerstone in scientific modeling, yet the process of fine-tuning simulation parameters poses significant challenges. Conventionally, parameter adjustment relies on extensive numerical simulations, data analysis, and expert insights, resulting in substantial computational costs and low efficiency. The emergence of deep learning in recent years has provided promising avenues for more efficient exploration of parameter spaces. However, existing approaches often lack intuitive methods for precise parameter adjustment and optimization. To tackle these challenges, we introduce ParamsDrag, a model that facilitates parameter space exploration through direct interaction with visualizations. Inspired by DragGAN, our ParamsDrag model operates in three steps. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes. Through experiments conducted on real-world simulations and comparisons with state-of-the-art deep learning-based approaches, we demonstrate the efficacy of our solution.

IEEE VIS 2024 Content: ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging

ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging

Guan Li - Computer Network Information Center, Chinese Academy of Sciences

Yang Liu - Beijing Forestry University

Guihua Shan - Computer Network Information Center, Chinese Academy of Sciences

Shiyu Cheng - Chinese Academy of Sciences

Weiqun Cao - Beijing Forestry University

Junpeng Wang - Visa Research

Ko-Chih Wang - National Taiwan Normal University

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Room: Bayshore I

2024-10-16T13:06:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T13:06:00Z
Exemplar figure, described by caption below
ParamsDrag is a surrogate model developed to enhance the exploration of parameter spaces through direct interaction with visualizations. It allows scientists to intuitively manipulate a feature of interest by dragging it to a desired location within a visualization, subsequently generating the corresponding image. Additionally, ParamsDrag can retrieve the simulation parameters that led to the generation of the selected image, thereby streamlining the process of parameter identification and adjustment.
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Keywords

parameter exploration, feature interaction, parameter inversion

Abstract

Numerical simulation serves as a cornerstone in scientific modeling, yet the process of fine-tuning simulation parameters poses significant challenges. Conventionally, parameter adjustment relies on extensive numerical simulations, data analysis, and expert insights, resulting in substantial computational costs and low efficiency. The emergence of deep learning in recent years has provided promising avenues for more efficient exploration of parameter spaces. However, existing approaches often lack intuitive methods for precise parameter adjustment and optimization. To tackle these challenges, we introduce ParamsDrag, a model that facilitates parameter space exploration through direct interaction with visualizations. Inspired by DragGAN, our ParamsDrag model operates in three steps. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes. Through experiments conducted on real-world simulations and comparisons with state-of-the-art deep learning-based approaches, we demonstrate the efficacy of our solution.

IEEE VIS 2024 Content: Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy

Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy

Xumeng Wang - Nankai University, Tianjin, China

Shuangcheng Jiao - Nankai University, Tianjin, China

Chris Bryan - Arizona State University, Tempe, United States

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Room: Bayshore II

2024-10-17T18:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:45:00Z
Exemplar figure, described by caption below
Defogger augments the ability of humans to explore and gain increased value from data while adhering to constraints of Differential privacy.
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Keywords

Differential privacy, Visual data analysis, Data exploration, Visualization for uncertainty illustration

Abstract

Differential privacy ensures the security of individual privacy but poses challenges to data exploration processes because the limited privacy budget incapacitates the flexibility of exploration and the noisy feedback of data requests leads to confusing uncertainty. In this study, we take the lead in describing corresponding exploration scenarios, including underlying requirements and available exploration strategies. To facilitate practical applications, we propose a visual analysis approach to the formulation of exploration strategies. Our approach applies a reinforcement learning model to provide diverse suggestions for exploration strategies according to the exploration intent of users. A novel visual design for representing uncertainty in correlation patterns is integrated into our prototype system to support the proposed approach. Finally, we implemented a user study and two case studies. The results of these studies verified that our approach can help develop strategies that satisfy the exploration intent of users.

IEEE VIS 2024 Content: Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy

Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy

Xumeng Wang - Nankai University, Tianjin, China

Shuangcheng Jiao - Nankai University, Tianjin, China

Chris Bryan - Arizona State University, Tempe, United States

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Room: Bayshore II

2024-10-17T18:45:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:45:00Z
Exemplar figure, described by caption below
Defogger augments the ability of humans to explore and gain increased value from data while adhering to constraints of Differential privacy.
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Keywords

Differential privacy, Visual data analysis, Data exploration, Visualization for uncertainty illustration

Abstract

Differential privacy ensures the security of individual privacy but poses challenges to data exploration processes because the limited privacy budget incapacitates the flexibility of exploration and the noisy feedback of data requests leads to confusing uncertainty. In this study, we take the lead in describing corresponding exploration scenarios, including underlying requirements and available exploration strategies. To facilitate practical applications, we propose a visual analysis approach to the formulation of exploration strategies. Our approach applies a reinforcement learning model to provide diverse suggestions for exploration strategies according to the exploration intent of users. A novel visual design for representing uncertainty in correlation patterns is integrated into our prototype system to support the proposed approach. Finally, we implemented a user study and two case studies. The results of these studies verified that our approach can help develop strategies that satisfy the exploration intent of users.

IEEE VIS 2024 Content: Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration

Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration

Jinrui Wang - The University of Edinburgh, Edinburgh, United Kingdom

Xinhuan Shu - Newcastle University, Newcastle Upon Tyne, United Kingdom

Benjamin Bach - Inria, Bordeaux, France. University of Edinburgh, Edinburgh, United Kingdom

Uta Hinrichs - University of Edinburgh, Edinburgh, United Kingdom

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Room: Bayshore II

2024-10-17T18:33:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:33:00Z
Exemplar figure, described by caption below
An overview of the paper 'Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration' by Jinrui Wang, Xinhuan Shu, Benjamin Bach, and Ute Hinrichs, featuring a backdrop of selected covers from the visualization atlas cases analyzed in the survey.
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Keywords

Visualization Atlases, Information Visualization, Data-driven Storytelling

Abstract

This paper defines, analyzes, and discusses the emerging genre of visualization atlases. We currently witness an increase in web-based, data-driven initiatives that call themselves “atlases” while explaining complex, contemporary issues through data and visualizations: climate change, sustainability, AI, or cultural discoveries. To understand this emerging genre and inform their design, study, and authoring support, we conducted a systematic analysis of 33 visualization atlases and semi-structured interviews with eight visualization atlas creators. Based on our results, we contribute (1) a definition of a visualization atlas as a compendium of (web) pages aimed at explaining and supporting exploration of data about a dedicated topic through data, visualizations and narration. (2) a set of design patterns of 8 design dimensions, (3) insights into the atlas creation from interviews and (4) the definition of 5 visualization atlas genres. We found that visualization atlases are unique in the way they combine i) exploratory visualization, ii) narrative elements from data-driven storytelling and iii) structured navigation mechanisms. They target a wide range of audiences with different levels of domain knowledge, acting as tools for study, communication, and discovery. We conclude with a discussion of current design practices and emerging questions around the ethics and potential real-world impact of visualization atlases, aimed to inform the design and study of visualization atlases.

IEEE VIS 2024 Content: Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration

Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration

Jinrui Wang - The University of Edinburgh, Edinburgh, United Kingdom

Xinhuan Shu - Newcastle University, Newcastle Upon Tyne, United Kingdom

Benjamin Bach - Inria, Bordeaux, France. University of Edinburgh, Edinburgh, United Kingdom

Uta Hinrichs - University of Edinburgh, Edinburgh, United Kingdom

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Room: Bayshore II

2024-10-17T18:33:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:33:00Z
Exemplar figure, described by caption below
An overview of the paper 'Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration' by Jinrui Wang, Xinhuan Shu, Benjamin Bach, and Ute Hinrichs, featuring a backdrop of selected covers from the visualization atlas cases analyzed in the survey.
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Keywords

Visualization Atlases, Information Visualization, Data-driven Storytelling

Abstract

This paper defines, analyzes, and discusses the emerging genre of visualization atlases. We currently witness an increase in web-based, data-driven initiatives that call themselves “atlases” while explaining complex, contemporary issues through data and visualizations: climate change, sustainability, AI, or cultural discoveries. To understand this emerging genre and inform their design, study, and authoring support, we conducted a systematic analysis of 33 visualization atlases and semi-structured interviews with eight visualization atlas creators. Based on our results, we contribute (1) a definition of a visualization atlas as a compendium of (web) pages aimed at explaining and supporting exploration of data about a dedicated topic through data, visualizations and narration. (2) a set of design patterns of 8 design dimensions, (3) insights into the atlas creation from interviews and (4) the definition of 5 visualization atlas genres. We found that visualization atlases are unique in the way they combine i) exploratory visualization, ii) narrative elements from data-driven storytelling and iii) structured navigation mechanisms. They target a wide range of audiences with different levels of domain knowledge, acting as tools for study, communication, and discovery. We conclude with a discussion of current design practices and emerging questions around the ethics and potential real-world impact of visualization atlases, aimed to inform the design and study of visualization atlases.

IEEE VIS 2024 Content: User Experience of Visualizations in Motion: A Case Study and Design Considerations

User Experience of Visualizations in Motion: A Case Study and Design Considerations

Lijie Yao - Xi'an Jiaotong-Liverpool University, Suzhou, China. Université Paris-Saclay, CNRS, Inria, Gif-sur-Yvette, France

Federica Bucchieri - Univerisité Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Victoria McArthur - Carleton University, Ottawa, Canada

Anastasia Bezerianos - LISN, Université Paris-Saclay, CNRS, INRIA, Orsay, France

Petra Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

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Room: Bayshore III

2024-10-17T18:09:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:09:00Z
Exemplar figure, described by caption below
Three situated visualizations tested in our game RobotLife: Left - a horizontal bar chart positioned outside of the game enemy character, Center - a vertical bar chart integrated in the texture of the game enemy character, and Right - a circular bar chart (donut chart) partially match to the design of game enemy character.
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Keywords

Situated visualization, visualization in motion, design considerations

Abstract

We present a systematic review, an empirical study, and a first set of considerations for designing visualizations in motion, derived from a concrete scenario in which these visualizations were used to support a primary task. In practice, when viewers are confronted with embedded visualizations, they often have to focus on a primary task and can only quickly glance at a visualization showing rich, often dynamically updated, information. As such, the visualizations must be designed so as not to distract from the primary task, while at the same time being readable and useful for aiding the primary task. For example, in games, players who are engaged in a battle have to look at their enemies but also read the remaining health of their own game character from the health bar over their character's head. Many trade-offs are possible in the design of embedded visualizations in such dynamic scenarios, which we explore in-depth in this paper with a focus on user experience. We use video games as an example of an application context with a rich existing set of visualizations in motion. We begin our work with a systematic review of in-game visualizations in motion. Next, we conduct an empirical user study to investigate how different embedded visualizations in motion designs impact user experience. We conclude with a set of considerations and trade-offs for designing visualizations in motion more broadly as derived from what we learned about video games. All supplemental materials of this paper are available at osf.io/3v8wm/.

IEEE VIS 2024 Content: User Experience of Visualizations in Motion: A Case Study and Design Considerations

User Experience of Visualizations in Motion: A Case Study and Design Considerations

Lijie Yao - Xi'an Jiaotong-Liverpool University, Suzhou, China. Université Paris-Saclay, CNRS, Inria, Gif-sur-Yvette, France

Federica Bucchieri - Univerisité Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Victoria McArthur - Carleton University, Ottawa, Canada

Anastasia Bezerianos - LISN, Université Paris-Saclay, CNRS, INRIA, Orsay, France

Petra Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

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Room: Bayshore III

2024-10-17T18:09:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T18:09:00Z
Exemplar figure, described by caption below
Three situated visualizations tested in our game RobotLife: Left - a horizontal bar chart positioned outside of the game enemy character, Center - a vertical bar chart integrated in the texture of the game enemy character, and Right - a circular bar chart (donut chart) partially match to the design of game enemy character.
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Keywords

Situated visualization, visualization in motion, design considerations

Abstract

We present a systematic review, an empirical study, and a first set of considerations for designing visualizations in motion, derived from a concrete scenario in which these visualizations were used to support a primary task. In practice, when viewers are confronted with embedded visualizations, they often have to focus on a primary task and can only quickly glance at a visualization showing rich, often dynamically updated, information. As such, the visualizations must be designed so as not to distract from the primary task, while at the same time being readable and useful for aiding the primary task. For example, in games, players who are engaged in a battle have to look at their enemies but also read the remaining health of their own game character from the health bar over their character's head. Many trade-offs are possible in the design of embedded visualizations in such dynamic scenarios, which we explore in-depth in this paper with a focus on user experience. We use video games as an example of an application context with a rich existing set of visualizations in motion. We begin our work with a systematic review of in-game visualizations in motion. Next, we conduct an empirical user study to investigate how different embedded visualizations in motion designs impact user experience. We conclude with a set of considerations and trade-offs for designing visualizations in motion more broadly as derived from what we learned about video games. All supplemental materials of this paper are available at osf.io/3v8wm/.

IEEE VIS 2024 Content: A Practical Solver for Scalar Data Topological Simplification

A Practical Solver for Scalar Data Topological Simplification

Mohamed KISSI - CNRS, Paris, France. SORBONNE UNIVERSITE, Paris, France

Mathieu Pont - CNRS, Paris, France. Sorbonne Université, Paris, France

Joshua A Levine - University of Arizona, Tucson, United States

Julien Tierny - CNRS, Paris, France. Sorbonne Université, Paris, France

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Room: Bayshore VI

2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
Topological simplification of a dark matter density field in a cosmology dataset. The cosmic web geometry is depicted by an isosurface at isovalue 0.4, with core filament structures extracted via upward discrete integral lines from 2-saddles above 0.4. Our approach reduced the number of undesired topological features by 92%, leading to a less cluttered visualization. This simplifies the topology, removing noisy components and small-scale handles, as shown in the inset zooms. This also results in fewer skips in persistent saddle connector reversals, revealing the primary filament structure more clearly.
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Keywords

Topological Data Analysis, scalar data, simplification, feature extraction.

Abstract

This paper presents a practical approach for the optimization of topological simplification, a central pre-processing step for the analysis and visualization of scalar data. Given an input scalar field f and a set of “signal” persistence pairs to maintain, our approaches produces an output field g that is close to f and which optimizes (i) the cancellation of “non-signal” pairs, while (ii) preserving the “signal” pairs. In contrast to pre-existing simplification approaches, our method is not restricted to persistence pairs involving extrema and can thus address a larger class of topological features, in particular saddle pairs in three-dimensional scalar data. Our approach leverages recent generic persistence optimization frameworks and extends them with tailored accelerations specific to the problem of topological simplification. Extensive experiments report substantial accelerations over these frameworks, thereby making topological simplification optimization practical for real-life datasets. Our work enables a direct visualization and analysis of the topologically simplified data, e.g., via isosurfaces of simplified topology (fewer components and handles). We apply our approach to the extraction of prominent filament structures in three-dimensional data. Specifically, we show that our pre-simplification of the data leads to practical improvements over standard topological techniques for removing filament loops. We also show how our framework can be used to repair genus defects in surface processing. Finally, we provide a C++ implementation for reproducibility purposes.

IEEE VIS 2024 Content: A Practical Solver for Scalar Data Topological Simplification

A Practical Solver for Scalar Data Topological Simplification

Mohamed KISSI - CNRS, Paris, France. SORBONNE UNIVERSITE, Paris, France

Mathieu Pont - CNRS, Paris, France. Sorbonne Université, Paris, France

Joshua A Levine - University of Arizona, Tucson, United States

Julien Tierny - CNRS, Paris, France. Sorbonne Université, Paris, France

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Room: Bayshore VI

2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
Topological simplification of a dark matter density field in a cosmology dataset. The cosmic web geometry is depicted by an isosurface at isovalue 0.4, with core filament structures extracted via upward discrete integral lines from 2-saddles above 0.4. Our approach reduced the number of undesired topological features by 92%, leading to a less cluttered visualization. This simplifies the topology, removing noisy components and small-scale handles, as shown in the inset zooms. This also results in fewer skips in persistent saddle connector reversals, revealing the primary filament structure more clearly.
Fast forward
Keywords

Topological Data Analysis, scalar data, simplification, feature extraction.

Abstract

This paper presents a practical approach for the optimization of topological simplification, a central pre-processing step for the analysis and visualization of scalar data. Given an input scalar field f and a set of “signal” persistence pairs to maintain, our approaches produces an output field g that is close to f and which optimizes (i) the cancellation of “non-signal” pairs, while (ii) preserving the “signal” pairs. In contrast to pre-existing simplification approaches, our method is not restricted to persistence pairs involving extrema and can thus address a larger class of topological features, in particular saddle pairs in three-dimensional scalar data. Our approach leverages recent generic persistence optimization frameworks and extends them with tailored accelerations specific to the problem of topological simplification. Extensive experiments report substantial accelerations over these frameworks, thereby making topological simplification optimization practical for real-life datasets. Our work enables a direct visualization and analysis of the topologically simplified data, e.g., via isosurfaces of simplified topology (fewer components and handles). We apply our approach to the extraction of prominent filament structures in three-dimensional data. Specifically, we show that our pre-simplification of the data leads to practical improvements over standard topological techniques for removing filament loops. We also show how our framework can be used to repair genus defects in surface processing. Finally, we provide a C++ implementation for reproducibility purposes.

IEEE VIS 2024 Content: DracoGPT: Extracting Visualization Design Preferences from Large Language Models

DracoGPT: Extracting Visualization Design Preferences from Large Language Models

Huichen Will Wang - University of Washington, Seattle, United States

Mitchell L. Gordon - University of Washington, Seattle, United States

Leilani Battle - University of Washington, Seattle, United States

Jeffrey Heer - University of Washington, Seattle, United States

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Room: Bayshore II

2024-10-17T12:42:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T12:42:00Z
Exemplar figure, described by caption below
DracoGPT is a method for extracting, modeling, and assessing visualization design preferences from LLMs. We develop two pipelines--DracoGPT-Rank and DracoGPT-Recommend--to model LLMs prompted to either rank or recommend visual encoding specifications. We use Draco as a shared knowledge base in which to represent LLM design preferences and compare them to best practices from empirical research. The image shown summarizes the pipeline for DracoGPT-Rank.
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Keywords

Visualization, Large Language Models, Visualization Recommendation, Graphical Perception

Abstract

Trained on vast corpora, Large Language Models (LLMs) have the potential to encode visualization design knowledge and best practices. However, if they fail to do so, they might provide unreliable visualization recommendations. What visualization design preferences, then, have LLMs learned? We contribute DracoGPT, a method for extracting, modeling, and assessing visualization design preferences from LLMs. To assess varied tasks, we develop two pipelines--DracoGPT-Rank and DracoGPT-Recommend--to model LLMs prompted to either rank or recommend visual encoding specifications. We use Draco as a shared knowledge base in which to represent LLM design preferences and compare them to best practices from empirical research. We demonstrate that DracoGPT can accurately model the preferences expressed by LLMs, enabling analysis in terms of Draco design constraints. Across a suite of backing LLMs, we find that DracoGPT-Rank and DracoGPT-Recommend moderately agree with each other, but both substantially diverge from guidelines drawn from human subjects experiments. Future work can build on our approach to expand Draco's knowledge base to model a richer set of preferences and to provide a robust and cost-effective stand-in for LLMs.

IEEE VIS 2024 Content: DracoGPT: Extracting Visualization Design Preferences from Large Language Models

DracoGPT: Extracting Visualization Design Preferences from Large Language Models

Huichen Will Wang - University of Washington, Seattle, United States

Mitchell L. Gordon - University of Washington, Seattle, United States

Leilani Battle - University of Washington, Seattle, United States

Jeffrey Heer - University of Washington, Seattle, United States

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Room: Bayshore II

2024-10-17T12:42:00Z GMT-0600 Change your timezone on the schedule page
2024-10-17T12:42:00Z
Exemplar figure, described by caption below
DracoGPT is a method for extracting, modeling, and assessing visualization design preferences from LLMs. We develop two pipelines--DracoGPT-Rank and DracoGPT-Recommend--to model LLMs prompted to either rank or recommend visual encoding specifications. We use Draco as a shared knowledge base in which to represent LLM design preferences and compare them to best practices from empirical research. The image shown summarizes the pipeline for DracoGPT-Rank.
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Keywords

Visualization, Large Language Models, Visualization Recommendation, Graphical Perception

Abstract

Trained on vast corpora, Large Language Models (LLMs) have the potential to encode visualization design knowledge and best practices. However, if they fail to do so, they might provide unreliable visualization recommendations. What visualization design preferences, then, have LLMs learned? We contribute DracoGPT, a method for extracting, modeling, and assessing visualization design preferences from LLMs. To assess varied tasks, we develop two pipelines--DracoGPT-Rank and DracoGPT-Recommend--to model LLMs prompted to either rank or recommend visual encoding specifications. We use Draco as a shared knowledge base in which to represent LLM design preferences and compare them to best practices from empirical research. We demonstrate that DracoGPT can accurately model the preferences expressed by LLMs, enabling analysis in terms of Draco design constraints. Across a suite of backing LLMs, we find that DracoGPT-Rank and DracoGPT-Recommend moderately agree with each other, but both substantially diverge from guidelines drawn from human subjects experiments. Future work can build on our approach to expand Draco's knowledge base to model a richer set of preferences and to provide a robust and cost-effective stand-in for LLMs.

IEEE VIS 2024 Content: Towards Dataset-scale and Feature-oriented Evaluation of Text Summarization in Large Language Model Prompts

Towards Dataset-scale and Feature-oriented Evaluation of Text Summarization in Large Language Model Prompts

Sam Yu-Te Lee - University of California Davis, Davis, United States

Aryaman Bahukhandi - University of California, Davis, Davis, United States

Dongyu Liu - University of California at Davis, Davis, United States

Kwan-Liu Ma - University of California at Davis, Davis, United States

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Room: Bayshore I

2024-10-16T16:48:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:48:00Z
Exemplar figure, described by caption below
Bubble Plot, the key visualization in Awesum, designed to show prompt performance. Yellow curves suggest improvements, and purple curves suggest deterioration. The image suggests a mixed performance.
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Keywords

Visual analytics, prompt engineering, text summarization, human-computer interaction, dimensionality reduction

Abstract

Recent advancements in Large Language Models (LLMs) and Prompt Engineering have made chatbot customization more accessible, significantly reducing barriers to tasks that previously required programming skills. However, prompt evaluation, especially at the dataset scale, remains complex due to the need to assess prompts across thousands of test instances within a dataset. Our study, based on a comprehensive literature review and pilot study, summarized five critical challenges in prompt evaluation. In response, we introduce a feature-oriented workflow for systematic prompt evaluation. In the context of text summarization, our workflow advocates evaluation with summary characteristics (feature metrics) such as complexity, formality, or naturalness, instead of using traditional quality metrics like ROUGE. This design choice enables a more user-friendly evaluation of prompts, as it guides users in sorting through the ambiguity inherent in natural language. To support this workflow, we introduce Awesum, a visual analytics system that facilitates identifying optimal prompt refinements for text summarization through interactive visualizations, featuring a novel Prompt Comparator design that employs a BubbleSet-inspired design enhanced by dimensionality reduction techniques. We evaluate the xeffectiveness and general applicability of the system with practitioners from various domains and found that (1) our design helps overcome the learning curve for non-technical people to conduct a systematic evaluation of summarization prompts, and (2) our feature-oriented workflow has the potential to generalize to other NLG and image-generation tasks. For future works, we advocate moving towards feature-oriented evaluation of LLM prompts and discuss unsolved challenges in terms of human-agent interaction.

IEEE VIS 2024 Content: Towards Dataset-scale and Feature-oriented Evaluation of Text Summarization in Large Language Model Prompts

Towards Dataset-scale and Feature-oriented Evaluation of Text Summarization in Large Language Model Prompts

Sam Yu-Te Lee - University of California Davis, Davis, United States

Aryaman Bahukhandi - University of California, Davis, Davis, United States

Dongyu Liu - University of California at Davis, Davis, United States

Kwan-Liu Ma - University of California at Davis, Davis, United States

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Room: Bayshore I

2024-10-16T16:48:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:48:00Z
Exemplar figure, described by caption below
Bubble Plot, the key visualization in Awesum, designed to show prompt performance. Yellow curves suggest improvements, and purple curves suggest deterioration. The image suggests a mixed performance.
Fast forward
Keywords

Visual analytics, prompt engineering, text summarization, human-computer interaction, dimensionality reduction

Abstract

Recent advancements in Large Language Models (LLMs) and Prompt Engineering have made chatbot customization more accessible, significantly reducing barriers to tasks that previously required programming skills. However, prompt evaluation, especially at the dataset scale, remains complex due to the need to assess prompts across thousands of test instances within a dataset. Our study, based on a comprehensive literature review and pilot study, summarized five critical challenges in prompt evaluation. In response, we introduce a feature-oriented workflow for systematic prompt evaluation. In the context of text summarization, our workflow advocates evaluation with summary characteristics (feature metrics) such as complexity, formality, or naturalness, instead of using traditional quality metrics like ROUGE. This design choice enables a more user-friendly evaluation of prompts, as it guides users in sorting through the ambiguity inherent in natural language. To support this workflow, we introduce Awesum, a visual analytics system that facilitates identifying optimal prompt refinements for text summarization through interactive visualizations, featuring a novel Prompt Comparator design that employs a BubbleSet-inspired design enhanced by dimensionality reduction techniques. We evaluate the xeffectiveness and general applicability of the system with practitioners from various domains and found that (1) our design helps overcome the learning curve for non-technical people to conduct a systematic evaluation of summarization prompts, and (2) our feature-oriented workflow has the potential to generalize to other NLG and image-generation tasks. For future works, we advocate moving towards feature-oriented evaluation of LLM prompts and discuss unsolved challenges in terms of human-agent interaction.

IEEE VIS 2024 Content: Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

Arvind Srinivasan - Aarhus University, Aarhus, Denmark

Johannes Ellemose - Aarhus University, Aarhus N, Denmark

Peter W. S. Butcher - Bangor University, Bangor, United Kingdom

Panagiotis D. Ritsos - Bangor University, Bangor, United Kingdom

Niklas Elmqvist - Aarhus University, Aarhus, Denmark

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Room: Bayshore II

2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:00:00Z
Exemplar figure, described by caption below
This image illustrates various Attention-aware re-visualization techniques that adapt based on user attention in both 3D and 2D spaces. The left side of the image focuses on our “Data Aware 3D” implementation applying GPU Color Picking, featuring heatmaps and desaturation techniques that respond to user orientation, rotation, and location within a 3D environment. The right side displays our “Data Agnostic 2D” implementation applying a Picture Framing Metaphor, highlighting how user attention, tracked through gaze, pointer, and keyboard input, shapes different frames like bar, area, and heat maps. These revisualizations that adjust dynamically to emphasize areas of interest based on cumulative attention were then qualitatively evaluated across different triggering mechanisms.
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Keywords

Attention tracking, eyetracking, immersive analytics, ubiquitous analytics, post-WIMP interaction

Abstract

We propose the notion of attention-aware visualizations (AAVs) that track the user’s perception of a visual representation over time and feed this information back to the visualization. Such context awareness is particularly useful for ubiquitous and immersive analytics where knowing which embedded visualizations the user is looking at can be used to make visualizations react appropriately to the user’s attention: for example, by highlighting data the user has not yet seen. We can separate the approach into three components: (1) measuring the user’s gaze on a visualization and its parts; (2) tracking the user’s attention over time; and (3) reactively modifying the visual representation based on the current attention metric. In this paper, we present two separate implementations of AAV: a 2D data-agnostic method for web-based visualizations that can use an embodied eyetracker to capture the user’s gaze, and a 3D data-aware one that uses the stencil buffer to track the visibility of each individual mark in a visualization. Both methods provide similar mechanisms for accumulating attention over time and changing the appearance of marks in response. We also present results from a qualitative evaluation studying visual feedback and triggering mechanisms for capturing and revisualizing attention.

IEEE VIS 2024 Content: Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

Arvind Srinivasan - Aarhus University, Aarhus, Denmark

Johannes Ellemose - Aarhus University, Aarhus N, Denmark

Peter W. S. Butcher - Bangor University, Bangor, United Kingdom

Panagiotis D. Ritsos - Bangor University, Bangor, United Kingdom

Niklas Elmqvist - Aarhus University, Aarhus, Denmark

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Room: Bayshore II

2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T17:00:00Z
Exemplar figure, described by caption below
This image illustrates various Attention-aware re-visualization techniques that adapt based on user attention in both 3D and 2D spaces. The left side of the image focuses on our “Data Aware 3D” implementation applying GPU Color Picking, featuring heatmaps and desaturation techniques that respond to user orientation, rotation, and location within a 3D environment. The right side displays our “Data Agnostic 2D” implementation applying a Picture Framing Metaphor, highlighting how user attention, tracked through gaze, pointer, and keyboard input, shapes different frames like bar, area, and heat maps. These revisualizations that adjust dynamically to emphasize areas of interest based on cumulative attention were then qualitatively evaluated across different triggering mechanisms.
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Keywords

Attention tracking, eyetracking, immersive analytics, ubiquitous analytics, post-WIMP interaction

Abstract

We propose the notion of attention-aware visualizations (AAVs) that track the user’s perception of a visual representation over time and feed this information back to the visualization. Such context awareness is particularly useful for ubiquitous and immersive analytics where knowing which embedded visualizations the user is looking at can be used to make visualizations react appropriately to the user’s attention: for example, by highlighting data the user has not yet seen. We can separate the approach into three components: (1) measuring the user’s gaze on a visualization and its parts; (2) tracking the user’s attention over time; and (3) reactively modifying the visual representation based on the current attention metric. In this paper, we present two separate implementations of AAV: a 2D data-agnostic method for web-based visualizations that can use an embodied eyetracker to capture the user’s gaze, and a 3D data-aware one that uses the stencil buffer to track the visibility of each individual mark in a visualization. Both methods provide similar mechanisms for accumulating attention over time and changing the appearance of marks in response. We also present results from a qualitative evaluation studying visual feedback and triggering mechanisms for capturing and revisualizing attention.

IEEE VIS 2024 Content: Conference room - Bayshore I

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Visualization and Automation in Data Science: Exploring the Paradox of Humans-in-the-Loop

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Towards a Visual Perception-Based Analysis of Clustering Quality Metrics

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Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems

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The State of Reproducibility Stamps for Visualization Research Papers

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Design-Specific Transforms In Visualization

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Merits and Limits of Preregistration for Visualization Research

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Visualization Artifacts are Boundary Objects

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Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network

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Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts

Mohammad Ghoniem

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Quality Metrics and Reordering Strategies for Revealing Patterns in BioFabric Visualizations

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On Network Structural and Temporal Encodings: A Space and Time Odyssey

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MoNetExplorer: A Visual Analytics System for Analyzing Dynamic Networks with Temporal Network Motifs

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Topological Separation of Vortices

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KMTLabeler: An Interactive Knowledge-Assisted Labeling Tool for Medical Text Classification

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HuBar: A Visual Analytics Tool to Explore Human Behaviour based on fNIRS in AR guidance systems

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How Does Automation Shape the Process of Narrative Visualization: A Survey of Tools

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A Survey on Progressive Visualization

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Towards Reusable and Reactive Widgets for Information Visualization Research and Dissemination

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Towards Enhancing Low Vision Usability of Data Charts on Smartphones

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When Refreshable Tactile Displays Meet Conversational Agents: Investigating Accessible Data Presentation and Analysis with Touch and Speech

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Touching the Ground: Evaluating the Effectiveness of Data Physicalizations for Spatial Data Analysis Tasks

Bridger Herman

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Evaluating Force-based Haptics for Immersive Tangible Interactions with Surface Visualizations

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SpatialTouch: Exploring Spatial Data Visualizations in Cross-reality

Lixiang Zhao

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Test of Time Awards

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IEEE VIS 2024 Content: Conference room - Bayshore I

Bayshore I

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The Matrix Arcade: A Visual Explorable of Matrix Transformations

Yi Zhe Ang

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Panda or Gibbon? A Beginner's Introduction to Adversarial Attacks

Yuzhe You

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

TalkToRanker: A Conversational Interface for Ranking-based Decision-Making

Conor Fitzpatrick

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Inside an interpretable-by-design machine learning model: enabling RNA splicing rational design

Shiwen Zhu

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

What Can a Node Learn from Its Neighbors in Graph Neural Networks?

Qianwen Wang

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Where is the information in data?

Kieran Murphy

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

A Visual Tour to Empirical Neural Network Robustness

Chen Chen

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Explaining Text-to-Command Conversational Models

Petar Stupar

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Can Large Language Models Explain Their Internal Mechanisms?

Nada Hussein

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

The Illustrated AlphaFold

Elana P Simon

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

ExplainPrompt: Decoding the language of AI prompts

Shawn Simister

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Explainability Perspectives on a Vision Transformer: From Global Architecture to Single Neuron

Anne Marx

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VDS: Visualization in Data Science Symposium

https://ieeevis.org/year/2024/program/event_s-vds.html

Session chair: Ana Crisan, Dylan Cashman, Saugat Pandey, Alvitta Ottley, John E Wenskovitch

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent

Maximilian T. Fischer

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visualization and Automation in Data Science: Exploring the Paradox of Humans-in-the-Loop

Jen Rogers

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

The Categorical Data Map: A Multidimensional Scaling-Based Approach

Frederik L. Dennig

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Towards a Visual Perception-Based Analysis of Clustering Quality Metrics

Graziano Blasilli

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems

Yongsu Ahn

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Seeing the Shift: Keep an Eye on Semantic Changes in Times of LLMs

Raphael Buchmüller

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization: BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization (Session 1)

https://ieeevis.org/year/2024/program/event_w-beliv.html

Session chair: Anastasia Bezerianos, Michael Correll, Kyle Hall, Jürgen Bernard, Dan Keefe, Mai Elshehaly, Mahsan Nourani

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Normalized Stress is Not Normalized: How to Interpret Stress Correctly

Jacob Miller

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Tasks and Telephone: Understanding Barriers to Inference due to Issues in Experiment Design

Abhraneel Sarma

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Old Wine in a New Bottle? Analysis of Visual Lineups with Signal Detection Theory

Sheng Long

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visualising Lived Experience: Learning from a Master andAlternative Narrative Framing

Mai Elshehaly

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Exploring Subjective Notions of Explainability through Counterfactual Visualization of Sentiment Analysis

Anamaria Crisan

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Position paper: Proposing the use of an “Advocatus Diaboli” as a pragmatic approach to improve transparency in qualitative data analysis and reporting

Judith Friedl-Knirsch

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization: BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization (Sesssion 2)

https://ieeevis.org/year/2024/program/event_w-beliv.html

Session chair: Anastasia Bezerianos, Michael Correll, Kyle Hall, Jürgen Bernard, Dan Keefe, Mai Elshehaly, Mahsan Nourani

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

The State of Reproducibility Stamps for Visualization Research Papers

Tobias Isenberg

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Striking the Right Balance: Systematic Assessment of Evaluation Method Distribution Across Contribution Types

Arran Zeyu Wang

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Testing the Test: Observations When Assessing Visualization Literacy of Domain Experts

Seyda Öney

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Design-Specific Transforms In Visualization

eugene Wu

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

The Role of Metacognition in Understanding Deceptive Bar Charts

Antonia Schlieder

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Merits and Limits of Preregistration for Visualization Research

Lonni Besançon

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visualization Artifacts are Boundary Objects

Jasmine Otto

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

[position paper] The Visualization JUDGE : Can Multimodal Foundation Models Guide Visualization Design Through Visual Perception?

Matthew Berger

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

We Don't Know How to Assess LLM Contributions in VIS/HCI

Anamaria Crisan

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Bridging Quantitative and Qualitative Methods for Visualization Research: A Data/Semantics Perspective in the Light of Advanced AI

Daniel Weiskopf

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Complexity as Design Material

Michael Correll

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Machine Learning for Visualization

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Joshua Levine

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

KD-INR: Time-Varying Volumetric Data Compression via Knowledge Distillation-based Implicit Neural Representation

Han Jun

2024-10-16T12:30:00Z – 2024-10-16T12:42:00Z GMT-0600 Change your timezone on the schedule page

Improving Efficiency of Iso-Surface Extraction on Implicit Neural Representations Using Uncertainty Propagation

Haoyu Li

2024-10-16T12:42:00Z – 2024-10-16T12:54:00Z GMT-0600 Change your timezone on the schedule page

StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization

Kaiyuan Tang

2024-10-16T12:54:00Z – 2024-10-16T13:06:00Z GMT-0600 Change your timezone on the schedule page

ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging

Guan Li

2024-10-16T13:06:00Z – 2024-10-16T13:18:00Z GMT-0600 Change your timezone on the schedule page

SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification

Yuhan Duan

2024-10-16T13:18:00Z – 2024-10-16T13:30:00Z GMT-0600 Change your timezone on the schedule page

Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network

Tianyu Xiong

2024-10-16T13:30:00Z – 2024-10-16T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Biological Data Visualization

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Nils Gehlenborg

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map

Deng Luo

2024-10-16T14:15:00Z – 2024-10-16T14:27:00Z GMT-0600 Change your timezone on the schedule page

“Nanomatrix: Scalable Construction of Crowded Biological Environments”

Ruwayda Alharbi

2024-10-16T14:27:00Z – 2024-10-16T14:39:00Z GMT-0600 Change your timezone on the schedule page

InVADo: Interactive Visual Analysis of Molecular Docking Data

Michael Krone

2024-10-16T14:39:00Z – 2024-10-16T14:51:00Z GMT-0600 Change your timezone on the schedule page

Visualization for diagnostic review of copy number variants in complex DNA sequencing data

Emilia Ståhlbom

2024-10-16T14:51:00Z – 2024-10-16T15:03:00Z GMT-0600 Change your timezone on the schedule page

Cell2Cell: Explorative Cell Interaction Analysis in Multi-Volumetric Tissue Data

Eric Mörth

2024-10-16T15:03:00Z – 2024-10-16T15:15:00Z GMT-0600 Change your timezone on the schedule page

Visual Support for the Loop Grafting Workflow on Proteins

Filip Opálený

2024-10-16T15:15:00Z – 2024-10-16T15:27:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: Natural Language and Multimodal Interaction

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Ana Crisan

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Learnable and Expressive Visualization Authoring Through Blended Interfaces

Sehi L'Yi

2024-10-16T16:00:00Z – 2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page

PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets

Jaeyoung Kim

2024-10-16T16:12:00Z – 2024-10-16T16:24:00Z GMT-0600 Change your timezone on the schedule page

LEVA: Using Large Language Models to Enhance Visual Analytics

Yuheng Zhao

2024-10-16T16:24:00Z – 2024-10-16T16:36:00Z GMT-0600 Change your timezone on the schedule page

ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language

Yuan Tian

2024-10-16T16:36:00Z – 2024-10-16T16:48:00Z GMT-0600 Change your timezone on the schedule page

Towards Dataset-scale and Feature-oriented Evaluation of Text Summarization in Large Language Model Prompts

Sam Yu-Te Lee

2024-10-16T16:48:00Z – 2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page

PrompTHis: Visualizing the Process and Influence of Prompt Editing during Text-to-Image Creation

Yuhan Guo

2024-10-16T17:00:00Z – 2024-10-16T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: Of Nodes and Networks

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Carolina Nobre

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts

Mohammad Ghoniem

2024-10-16T17:45:00Z – 2024-10-16T17:57:00Z GMT-0600 Change your timezone on the schedule page

Quality Metrics and Reordering Strategies for Revealing Patterns in BioFabric Visualizations

Johannes Fuchs

2024-10-16T17:57:00Z – 2024-10-16T18:09:00Z GMT-0600 Change your timezone on the schedule page

SpreadLine: Visualizing Egocentric Dynamic Influence

Yun-Hsin Kuo

2024-10-16T18:09:00Z – 2024-10-16T18:21:00Z GMT-0600 Change your timezone on the schedule page

On Network Structural and Temporal Encodings: A Space and Time Odyssey

Velitchko Filipov

2024-10-16T18:21:00Z – 2024-10-16T18:33:00Z GMT-0600 Change your timezone on the schedule page

MoNetExplorer: A Visual Analytics System for Analyzing Dynamic Networks with Temporal Network Motifs

Seokweon Jung

2024-10-16T18:33:00Z – 2024-10-16T18:45:00Z GMT-0600 Change your timezone on the schedule page

Evaluating and extending speedup techniques for optimal crossing minimization in layered graph drawings

Connor Wilson

2024-10-16T18:45:00Z – 2024-10-16T18:57:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: Embeddings and Document Spatialization

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Alex Endert

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Visualizing Temporal Topic Embeddings with a Compass

Daniel Palamarchuk

2024-10-17T12:30:00Z – 2024-10-17T12:42:00Z GMT-0600 Change your timezone on the schedule page

A General Framework for Comparing Embedding Visualizations Across Class-Label Hierarchies

Trevor Manz

2024-10-17T12:42:00Z – 2024-10-17T12:54:00Z GMT-0600 Change your timezone on the schedule page

ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map

Yilin Ye

2024-10-17T12:54:00Z – 2024-10-17T13:06:00Z GMT-0600 Change your timezone on the schedule page

A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations

Daniel Atzberger

2024-10-17T13:06:00Z – 2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page

PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings

Fabian Beck

2024-10-17T13:18:00Z – 2024-10-17T13:30:00Z GMT-0600 Change your timezone on the schedule page

De-cluttering Scatterplots with Integral Images

Hennes Rave

2024-10-17T13:30:00Z – 2024-10-17T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: Topological Data Analysis

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Ingrid Hotz

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

MSz: An Efficient Parallel Algorithm for Correcting Morse-Smale Segmentations in Error-Bounded Lossy Compressors

Yuxiao Li

2024-10-17T14:15:00Z – 2024-10-17T14:27:00Z GMT-0600 Change your timezone on the schedule page

Fast Comparative Analysis of Merge Trees Using Locality-Sensitive Hashing

Weiran Lyu

2024-10-17T14:27:00Z – 2024-10-17T14:39:00Z GMT-0600 Change your timezone on the schedule page

Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration

Mingzhe Li

2024-10-17T14:39:00Z – 2024-10-17T14:51:00Z GMT-0600 Change your timezone on the schedule page

Wasserstein Dictionaries of Persistence Diagrams

Keanu Sisouk

2024-10-17T14:51:00Z – 2024-10-17T15:03:00Z GMT-0600 Change your timezone on the schedule page

Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)

Julien Tierny

2024-10-17T15:03:00Z – 2024-10-17T15:15:00Z GMT-0600 Change your timezone on the schedule page

Topological Separation of Vortices

Adeel Zafar

2024-10-17T15:15:00Z – 2024-10-17T15:27:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: The Toolboxes of Visualization

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Dominik Moritz

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

KMTLabeler: An Interactive Knowledge-Assisted Labeling Tool for Medical Text Classification

Quan Li

2024-10-17T16:00:00Z – 2024-10-17T16:12:00Z GMT-0600 Change your timezone on the schedule page

TTK is Getting MPI-Ready

Eve Le Guillou

2024-10-17T16:12:00Z – 2024-10-17T16:24:00Z GMT-0600 Change your timezone on the schedule page

HuBar: A Visual Analytics Tool to Explore Human Behaviour based on fNIRS in AR guidance systems

Sonia Castelo Quispe

2024-10-17T16:24:00Z – 2024-10-17T16:36:00Z GMT-0600 Change your timezone on the schedule page

How Does Automation Shape the Process of Narrative Visualization: A Survey of Tools

Qing Chen

2024-10-17T16:36:00Z – 2024-10-17T16:48:00Z GMT-0600 Change your timezone on the schedule page

A Survey on Progressive Visualization

Alex Ulmer

2024-10-17T16:48:00Z – 2024-10-17T17:00:00Z GMT-0600 Change your timezone on the schedule page

Towards Reusable and Reactive Widgets for Information Visualization Research and Dissemination

John Alexis Guerra-Gomez

2024-10-17T17:00:00Z – 2024-10-17T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: Accessibility and Touch

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Narges Mahyar

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Beyond Vision Impairments: Redefining the Scope of Accessible Data Representations

Brianna Wimer

2024-10-17T17:45:00Z – 2024-10-17T17:57:00Z GMT-0600 Change your timezone on the schedule page

Towards Enhancing Low Vision Usability of Data Charts on Smartphones

Yash Prakash

2024-10-17T17:57:00Z – 2024-10-17T18:09:00Z GMT-0600 Change your timezone on the schedule page

When Refreshable Tactile Displays Meet Conversational Agents: Investigating Accessible Data Presentation and Analysis with Touch and Speech

Kim Marriott

2024-10-17T18:09:00Z – 2024-10-17T18:21:00Z GMT-0600 Change your timezone on the schedule page

Touching the Ground: Evaluating the Effectiveness of Data Physicalizations for Spatial Data Analysis Tasks

Bridger Herman

2024-10-17T18:21:00Z – 2024-10-17T18:33:00Z GMT-0600 Change your timezone on the schedule page

Evaluating Force-based Haptics for Immersive Tangible Interactions with Surface Visualizations

Hamza Afzaal

2024-10-17T18:33:00Z – 2024-10-17T18:45:00Z GMT-0600 Change your timezone on the schedule page

SpatialTouch: Exploring Spatial Data Visualizations in Cross-reality

Lixiang Zhao

2024-10-17T18:45:00Z – 2024-10-17T18:57:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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Conference Events: Test of Time Awards

https://ieeevis.org/year/2024/program/event_conf.html

Session chair: Ross Maciejewski

2024-10-18T14:15:00Z – 2024-10-18T15:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Test of Time Awards

Ross Maciejewski

2024-10-18T14:15:00Z – 2024-10-18T15:00:00Z GMT-0600 Change your timezone on the schedule page
IEEE VIS 2024 Content: Conference room - Bayshore II

Bayshore II

Current Session

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VAST Challenge

https://ieeevis.org/year/2024/program/event_a-vast-challenge.html

Session chair: R. Jordan Crouser, Steve Gomez, Jereme Haack

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Visual Analysis of Complex Temporal Networks Supported by Analytic Provenance

Yuhan Guo

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Tracking Overfishing: Visual Analytics of Suspicious Behaviors in Commercial Fishing Vessels

Juanpablo Andrew Heredia

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

LIST-Durant-MC1

Eloi Durant

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visual Anomaly Detection in Temporal Knowledge Graphs

Kevin Iselborn

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

UKON-MC2-1

Lisa-Maria Reutlinger

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

VAST 2024-MC2 Challenge

Sinem Bilge Guler

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

UKON-Buchmueller-MC1

Daniel Fürst

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Purdue-Chen-MC2

Hao Wang

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

FishEye Watcher: a visual analytics system for knowledge graph bias detection

Tian Qiu

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Utilizing TimeArcs and PageRank to Solve the 2024 VAST Challenge Mini Challenge 3

Ethan Wei

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

FishBiasLens: Integrating Large Language Models and Visual Analytics for Bias Detection

Dany Mauro Diaz Espino

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visualization for Oceanus' Fishing Market

Xueli Shu

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visual Analytics for Detecting Illegal Transport Activities

Yi Shan

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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LDAV: 13th IEEE Symposium on Large Data Analysis and Visualization: LDAV: 14th IEEE Symposium on Large Data Analysis and Visualization

https://ieeevis.org/year/2024/program/event_a-ldav.html

Session chair: Silvio Rizzi, Gunther Weber, Guido Reina, Ken Moreland

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Efficient Analysis and Visualization of High-Resolution Computed Tomography Data for the Exploration of Enclosed Cuneiform Tablets

Andreas Beckert

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions

Luca Reichmann

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Web-based Visualization and Analytics of Petascale data: Equity as a Tide that Lifts All Boats

Aashish Panta

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Distributed Path Compression for Piecewise Linear Morse-Smale Segmentations and Connected Components

Michael Will

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Standardized Data-Parallel Rendering Using ANARI

Ingo Wald

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Adaptive Multi-Resolution Encoding for Interactive Large-Scale Volume Visualization through Functional Approximation

Jianxin Sun

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

LLM4Vis: Large Language Models for Information Visualization

https://ieeevis.org/year/2024/program/event_t-llm4vis.html

Session chair: Enamul Hoque

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Current Session

Next Session

NLVIZ Workshop: Exploring Research Opportunities for Natural Language, Text, and Data Visualization

https://ieeevis.org/year/2024/program/event_w-nlviz.html

Session chair: Vidya Setlur, Arjun Srinivasan

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Steering LLM Summarization with Visual Workspaces for Sensemaking

Xuxin Tang

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Towards Real-Time Speech Segmentation for Glanceable Conversation Visualization

Shanna Li Ching Hollingworth

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

vitaLITy 2: Reviewing Academic Literature Using Large Language Models

Hongye An

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

“Show Me What’s Wrong!”: Combining Charts and Text to Guide Data Analysis

Beatriz Feliciano

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visualizing Spatial Semantics of Dimensionally Reduced Text Embeddings

Wei Liu

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Generating Analytic Specifications for Data Visualization from Natural Language Queries using Large Language Models

Rishab Mitra

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Towards Inline Natural Language Authoring for Word-Scale Visualizations

Paige So'Brien

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

iToT: An Interactive System for Customized Tree-of-Thought Generation

Alan Boyle

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Strategic management analysis: from data to strategy diagram by LLM

Richard Brath

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

A Preliminary Roadmap for LLMs as Visual Data Analysis Assistants

Ashley Suh

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Enhancing Arabic Poetic Structure Analysis through Visualization

Abdelmalek Berkani

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Immersive Visualization and Visual Analytics

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Lingyun Yu

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

CompositingVis: Exploring Interaction for Creating Composite Visualizations in Immersive Environments

Yalong Yang

2024-10-16T12:30:00Z – 2024-10-16T12:42:00Z GMT-0600 Change your timezone on the schedule page

This is the Table I Want! Interactive Data Transformation on Desktop and in Virtual Reality

Sungwon In

2024-10-16T12:42:00Z – 2024-10-16T12:54:00Z GMT-0600 Change your timezone on the schedule page

VoxAR: Adaptive Visualization of Volume Rendered Objects in Optical See-Through Augmented Reality

Saeed Boorboor

2024-10-16T12:54:00Z – 2024-10-16T13:06:00Z GMT-0600 Change your timezone on the schedule page

Precise Embodied Data Selection in Room-scale Visualisations While Retaining View Context

Lonni Besançon

2024-10-16T13:06:00Z – 2024-10-16T13:18:00Z GMT-0600 Change your timezone on the schedule page

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis

Adam Coscia

2024-10-16T13:18:00Z – 2024-10-16T13:30:00Z GMT-0600 Change your timezone on the schedule page

Eliciting Model Steering Interactions from Users via Data and Visual Design Probes

Anamaria Crisan

2024-10-16T13:30:00Z – 2024-10-16T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Judgment and Decision-making

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Wenwen Dou

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Decoupling Judgment and Decision Making: A Tale of Two Tails

Başak Oral

2024-10-16T14:15:00Z – 2024-10-16T14:27:00Z GMT-0600 Change your timezone on the schedule page

Unmasking Dunning-Kruger Effect in Visual Reasoning and Visual Data Analysis

Mengyu Chen

2024-10-16T14:27:00Z – 2024-10-16T14:39:00Z GMT-0600 Change your timezone on the schedule page

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

Ratanond Koonchanok

2024-10-16T14:39:00Z – 2024-10-16T14:51:00Z GMT-0600 Change your timezone on the schedule page

Causal Priors and Their Influence on Judgements of Causality in Visualized Data

Arran Zeyu Wang

2024-10-16T14:51:00Z – 2024-10-16T15:03:00Z GMT-0600 Change your timezone on the schedule page

KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts

Adam Coscia

2024-10-16T15:03:00Z – 2024-10-16T15:15:00Z GMT-0600 Change your timezone on the schedule page

What Do We Mean When We Say “Insight”? A Formal Synthesis of Existing Theory

Leilani Battle

2024-10-16T15:15:00Z – 2024-10-16T15:27:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Perception and Cognition

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Bernhard Preim

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

The Impact of Vertical Scaling on Normal Probability Density Function Plots

Racquel Fygenson

2024-10-16T16:00:00Z – 2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page

The Effect of Visual Aids on Reading Numeric Data Tables

Charles Perin

2024-10-16T16:12:00Z – 2024-10-16T16:24:00Z GMT-0600 Change your timezone on the schedule page

Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Charles Perin

2024-10-16T16:24:00Z – 2024-10-16T16:36:00Z GMT-0600 Change your timezone on the schedule page

Examining Limits of Small Multiples: Frame Quantity Impacts Judgments with Line Graphs

Helia Hosseinpour

2024-10-16T16:36:00Z – 2024-10-16T16:48:00Z GMT-0600 Change your timezone on the schedule page

Memory Recall for Data Visualizations in Mixed Reality, Virtual Reality, 3D, and 2D

Christophe Hurter

2024-10-16T16:48:00Z – 2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page

Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

Arvind Srinivasan , Johannes Ellemose

2024-10-16T17:00:00Z – 2024-10-16T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Designing Palettes and Encodings

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Khairi Rheda

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

GeoLinter: A Linting Framework for Choropleth Maps

Fan Lei

2024-10-16T17:45:00Z – 2024-10-16T17:57:00Z GMT-0600 Change your timezone on the schedule page

Mixing Linters with GUIs: A Color Palette Design Probe

Andrew M McNutt

2024-10-16T17:57:00Z – 2024-10-16T18:09:00Z GMT-0600 Change your timezone on the schedule page

Dynamic Color Assignment for Hierarchical Data

Weikai Yang

2024-10-16T18:09:00Z – 2024-10-16T18:21:00Z GMT-0600 Change your timezone on the schedule page

An Empirically Grounded Approach for Designing Shape Palettes

Chin Tseng

2024-10-16T18:21:00Z – 2024-10-16T18:33:00Z GMT-0600 Change your timezone on the schedule page

Effectiveness of Area-to-Value Legends and Grid Lines in Contiguous Area Cartograms

Michael Gastner

2024-10-16T18:33:00Z – 2024-10-16T18:45:00Z GMT-0600 Change your timezone on the schedule page

What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts

Cindy Xiong Bearfield

2024-10-16T18:45:00Z – 2024-10-16T18:57:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Visualization Recommendation

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Johannes Knittel

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data'

Songheng Zhang

2024-10-17T12:30:00Z – 2024-10-17T12:42:00Z GMT-0600 Change your timezone on the schedule page

DracoGPT: Extracting Visualization Design Preferences from Large Language Models

Huichen Will Wang

2024-10-17T12:42:00Z – 2024-10-17T12:54:00Z GMT-0600 Change your timezone on the schedule page

Chart2Vec: A Universal Embedding of Context-Aware Visualizations

Qing Chen

2024-10-17T12:54:00Z – 2024-10-17T13:06:00Z GMT-0600 Change your timezone on the schedule page

Agnostic Visual Recommendation Systems: Open Challenges and Future Directions

Luca Podo

2024-10-17T13:06:00Z – 2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page

D-Tour: Semi-Automatic Generation of Interactive Guided Tours for Visualization Dashboard Onboarding

Vaishali Dhanoa

2024-10-17T13:18:00Z – 2024-10-17T13:30:00Z GMT-0600 Change your timezone on the schedule page

Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes

Chen Chen

2024-10-17T13:30:00Z – 2024-10-17T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Visual Design: Sketching and Labeling

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Jonathan C. Roberts

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Discursive Patinas: Anchoring Discussions in Data Visualizations

Tobias Kauer

2024-10-17T14:15:00Z – 2024-10-17T14:27:00Z GMT-0600 Change your timezone on the schedule page

Active Gaze Labeling: Visualization for Trust Building

Maurice Koch

2024-10-17T14:27:00Z – 2024-10-17T14:39:00Z GMT-0600 Change your timezone on the schedule page

A Survey on Non-photorealistic Rendering Approaches for Point Cloud Visualization

Ole Wegen

2024-10-17T14:39:00Z – 2024-10-17T14:51:00Z GMT-0600 Change your timezone on the schedule page

Interactive Reweighting for Mitigating Label Quality Issues

Weikai Yang

2024-10-17T14:51:00Z – 2024-10-17T15:03:00Z GMT-0600 Change your timezone on the schedule page

Graph Transformer for Label Placement

Jingwei Qu

2024-10-17T15:03:00Z – 2024-10-17T15:15:00Z GMT-0600 Change your timezone on the schedule page

DataGarden: Formalizing Personal Sketches into Structured Visualization Templates

Anna Offenwanger

2024-10-17T15:15:00Z – 2024-10-17T15:27:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Visualization Design Methods

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Miriah Meyer

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

"It's a Good Idea to Put It Into Words": Writing 'Rudders' in the Initial Stages of Visualization Design

Chase Stokes

2024-10-17T16:00:00Z – 2024-10-17T16:12:00Z GMT-0600 Change your timezone on the schedule page

Unveiling How Examples Shape Data Visualization Design Outcomes

Hannah K. Bako

2024-10-17T16:12:00Z – 2024-10-17T16:24:00Z GMT-0600 Change your timezone on the schedule page

Practices and Strategies in Responsive Thematic Map Design: A Report from Design Workshops with Experts

Sarah Schöttler

2024-10-17T16:24:00Z – 2024-10-17T16:36:00Z GMT-0600 Change your timezone on the schedule page

Path-based Design Model for Constructing and Exploring Alternative Visualisations

Jonathan C Roberts

2024-10-17T16:36:00Z – 2024-10-17T16:48:00Z GMT-0600 Change your timezone on the schedule page

Mind Drifts, Data Shifts: Utilizing Mind Wandering to Track the Evolution of User Experience with Data Visualizations

Anjana Arunkumar

2024-10-17T16:48:00Z – 2024-10-17T17:00:00Z GMT-0600 Change your timezone on the schedule page

Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks

Astrid van den Brandt

2024-10-17T17:00:00Z – 2024-10-17T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: Journalism and Public Policy

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Sungahn Ko

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

More Than Data Stories: Broadening the Role of Visualization in Contemporary Journalism

Yu Fu

2024-10-17T17:45:00Z – 2024-10-17T17:57:00Z GMT-0600 Change your timezone on the schedule page

The Impact of Elicitation and Contrasting Narratives on Engagement, Recall and Attitude Change with News Articles Containing Data Visualization

Milad Rogha

2024-10-17T17:57:00Z – 2024-10-17T18:09:00Z GMT-0600 Change your timezone on the schedule page

What Can Interactive Visualization do for Participatory Budgeting in Chicago?

Alex Kale

2024-10-17T18:09:00Z – 2024-10-17T18:21:00Z GMT-0600 Change your timezone on the schedule page

The Backstory to “Swaying the Public”: A Design Chronicle of Election Forecast Visualizations

Fumeng Yang

2024-10-17T18:21:00Z – 2024-10-17T18:33:00Z GMT-0600 Change your timezone on the schedule page

Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration

Jinrui Wang

2024-10-17T18:33:00Z – 2024-10-17T18:45:00Z GMT-0600 Change your timezone on the schedule page

Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy

Xumeng Wang

2024-10-17T18:45:00Z – 2024-10-17T18:57:00Z GMT-0600 Change your timezone on the schedule page
IEEE VIS 2024 Content: Conference room - Bayshore II

Bayshore II

Current Session

Next Session

VAST Challenge

https://ieeevis.org/year/2024/program/event_a-vast-challenge.html

Session chair: R. Jordan Crouser, Steve Gomez, Jereme Haack

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Visual Analysis of Complex Temporal Networks Supported by Analytic Provenance

Yuhan Guo

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Tracking Overfishing: Visual Analytics of Suspicious Behaviors in Commercial Fishing Vessels

Juanpablo Andrew Heredia

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visual Anomaly Detection in Temporal Knowledge Graphs

Kevin Iselborn

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

VAST 2024-MC2 Challenge

Sinem Bilge Guler

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

UKON-Buchmueller-MC1

Daniel Fürst

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Purdue-Chen-MC2

Hao Wang

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

FishEye Watcher: a visual analytics system for knowledge graph bias detection

Tian Qiu

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Utilizing TimeArcs and PageRank to Solve the 2024 VAST Challenge Mini Challenge 3

Ethan Wei

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

FishBiasLens: Integrating Large Language Models and Visual Analytics for Bias Detection

Dany Mauro Diaz Espino

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visual Analytics for Detecting Illegal Transport Activities

Yi Shan

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

LDAV: 13th IEEE Symposium on Large Data Analysis and Visualization: LDAV: 14th IEEE Symposium on Large Data Analysis and Visualization

https://ieeevis.org/year/2024/program/event_a-ldav.html

Session chair: Silvio Rizzi, Gunther Weber, Guido Reina, Ken Moreland

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Efficient Analysis and Visualization of High-Resolution Computed Tomography Data for the Exploration of Enclosed Cuneiform Tablets

Andreas Beckert

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions

Luca Reichmann

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Web-based Visualization and Analytics of Petascale data: Equity as a Tide that Lifts All Boats

Aashish Panta

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Distributed Path Compression for Piecewise Linear Morse-Smale Segmentations and Connected Components

Michael Will

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Standardized Data-Parallel Rendering Using ANARI

Ingo Wald

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Adaptive Multi-Resolution Encoding for Interactive Large-Scale Volume Visualization through Functional Approximation

Jianxin Sun

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

LLM4Vis: Large Language Models for Information Visualization

https://ieeevis.org/year/2024/program/event_t-llm4vis.html

Session chair: Enamul Hoque

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Current Session

Next Session

NLVIZ Workshop: Exploring Research Opportunities for Natural Language, Text, and Data Visualization

https://ieeevis.org/year/2024/program/event_w-nlviz.html

Session chair: Vidya Setlur, Arjun Srinivasan

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Steering LLM Summarization with Visual Workspaces for Sensemaking

Xuxin Tang

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Towards Real-Time Speech Segmentation for Glanceable Conversation Visualization

Shanna Li Ching Hollingworth

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

vitaLITy 2: Reviewing Academic Literature Using Large Language Models

Hongye An

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

“Show Me What’s Wrong!”: Combining Charts and Text to Guide Data Analysis

Beatriz Feliciano

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visualizing Spatial Semantics of Dimensionally Reduced Text Embeddings

Wei Liu

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Generating Analytic Specifications for Data Visualization from Natural Language Queries using Large Language Models

Rishab Mitra

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Towards Inline Natural Language Authoring for Word-Scale Visualizations

Paige So'Brien

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

iToT: An Interactive System for Customized Tree-of-Thought Generation

Alan Boyle

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Strategic management analysis: from data to strategy diagram by LLM

Richard Brath

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

A Preliminary Roadmap for LLMs as Visual Data Analysis Assistants

Ashley Suh

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Enhancing Arabic Poetic Structure Analysis through Visualization

Abdelmalek Berkani

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: Immersive Visualization and Visual Analytics

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Lingyun Yu

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

CompositingVis: Exploring Interaction for Creating Composite Visualizations in Immersive Environments

Yalong Yang

2024-10-16T12:30:00Z – 2024-10-16T12:42:00Z GMT-0600 Change your timezone on the schedule page

This is the Table I Want! Interactive Data Transformation on Desktop and in Virtual Reality

Sungwon In

2024-10-16T12:42:00Z – 2024-10-16T12:54:00Z GMT-0600 Change your timezone on the schedule page

VoxAR: Adaptive Visualization of Volume Rendered Objects in Optical See-Through Augmented Reality

Saeed Boorboor

2024-10-16T12:54:00Z – 2024-10-16T13:06:00Z GMT-0600 Change your timezone on the schedule page

Precise Embodied Data Selection in Room-scale Visualisations While Retaining View Context

Lonni Besançon

2024-10-16T13:06:00Z – 2024-10-16T13:18:00Z GMT-0600 Change your timezone on the schedule page

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis

Adam Coscia

2024-10-16T13:18:00Z – 2024-10-16T13:30:00Z GMT-0600 Change your timezone on the schedule page

Eliciting Model Steering Interactions from Users via Data and Visual Design Probes

Anamaria Crisan

2024-10-16T13:30:00Z – 2024-10-16T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: Judgment and Decision-making

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Wenwen Dou

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Decoupling Judgment and Decision Making: A Tale of Two Tails

Başak Oral

2024-10-16T14:15:00Z – 2024-10-16T14:27:00Z GMT-0600 Change your timezone on the schedule page

Unmasking Dunning-Kruger Effect in Visual Reasoning and Visual Data Analysis

Mengyu Chen

2024-10-16T14:27:00Z – 2024-10-16T14:39:00Z GMT-0600 Change your timezone on the schedule page

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

Ratanond Koonchanok

2024-10-16T14:39:00Z – 2024-10-16T14:51:00Z GMT-0600 Change your timezone on the schedule page

Causal Priors and Their Influence on Judgements of Causality in Visualized Data

Arran Zeyu Wang

2024-10-16T14:51:00Z – 2024-10-16T15:03:00Z GMT-0600 Change your timezone on the schedule page

KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts

Adam Coscia

2024-10-16T15:03:00Z – 2024-10-16T15:15:00Z GMT-0600 Change your timezone on the schedule page

What Do We Mean When We Say “Insight”? A Formal Synthesis of Existing Theory

Leilani Battle

2024-10-16T15:15:00Z – 2024-10-16T15:27:00Z GMT-0600 Change your timezone on the schedule page

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VIS Full Papers: Perception and Cognition

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Tamara Munzner

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

The Impact of Vertical Scaling on Normal Probability Density Function Plots

Racquel Fygenson

2024-10-16T16:00:00Z – 2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page

The Effect of Visual Aids on Reading Numeric Data Tables

Charles Perin

2024-10-16T16:12:00Z – 2024-10-16T16:24:00Z GMT-0600 Change your timezone on the schedule page

Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Charles Perin

2024-10-16T16:24:00Z – 2024-10-16T16:36:00Z GMT-0600 Change your timezone on the schedule page

Examining Limits of Small Multiples: Frame Quantity Impacts Judgments with Line Graphs

Helia Hosseinpour

2024-10-16T16:36:00Z – 2024-10-16T16:48:00Z GMT-0600 Change your timezone on the schedule page

Memory Recall for Data Visualizations in Mixed Reality, Virtual Reality, 3D, and 2D

Christophe Hurter

2024-10-16T16:48:00Z – 2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page

Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

Arvind Srinivasan , Johannes Ellemose

2024-10-16T17:00:00Z – 2024-10-16T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Designing Palettes and Encodings

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Khairi Rheda

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

GeoLinter: A Linting Framework for Choropleth Maps

Fan Lei

2024-10-16T17:45:00Z – 2024-10-16T17:57:00Z GMT-0600 Change your timezone on the schedule page

Mixing Linters with GUIs: A Color Palette Design Probe

Andrew M McNutt

2024-10-16T17:57:00Z – 2024-10-16T18:09:00Z GMT-0600 Change your timezone on the schedule page

Dynamic Color Assignment for Hierarchical Data

Weikai Yang

2024-10-16T18:09:00Z – 2024-10-16T18:21:00Z GMT-0600 Change your timezone on the schedule page

An Empirically Grounded Approach for Designing Shape Palettes

Chin Tseng

2024-10-16T18:21:00Z – 2024-10-16T18:33:00Z GMT-0600 Change your timezone on the schedule page

Effectiveness of Area-to-Value Legends and Grid Lines in Contiguous Area Cartograms

Michael Gastner

2024-10-16T18:33:00Z – 2024-10-16T18:45:00Z GMT-0600 Change your timezone on the schedule page

What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts

Cindy Xiong Bearfield

2024-10-16T18:45:00Z – 2024-10-16T18:57:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Visualization Recommendation

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Johannes Knittel

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data'

Songheng Zhang

2024-10-17T12:30:00Z – 2024-10-17T12:42:00Z GMT-0600 Change your timezone on the schedule page

DracoGPT: Extracting Visualization Design Preferences from Large Language Models

Huichen Will Wang

2024-10-17T12:42:00Z – 2024-10-17T12:54:00Z GMT-0600 Change your timezone on the schedule page

Chart2Vec: A Universal Embedding of Context-Aware Visualizations

Qing Chen

2024-10-17T12:54:00Z – 2024-10-17T13:06:00Z GMT-0600 Change your timezone on the schedule page

Agnostic Visual Recommendation Systems: Open Challenges and Future Directions

Luca Podo

2024-10-17T13:06:00Z – 2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page

D-Tour: Semi-Automatic Generation of Interactive Guided Tours for Visualization Dashboard Onboarding

Vaishali Dhanoa

2024-10-17T13:18:00Z – 2024-10-17T13:30:00Z GMT-0600 Change your timezone on the schedule page

Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes

Chen Chen

2024-10-17T13:30:00Z – 2024-10-17T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Visual Design: Sketching and Labeling

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Jonathan C. Roberts

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Discursive Patinas: Anchoring Discussions in Data Visualizations

Tobias Kauer

2024-10-17T14:15:00Z – 2024-10-17T14:27:00Z GMT-0600 Change your timezone on the schedule page

Active Gaze Labeling: Visualization for Trust Building

Maurice Koch

2024-10-17T14:27:00Z – 2024-10-17T14:39:00Z GMT-0600 Change your timezone on the schedule page

A Survey on Non-photorealistic Rendering Approaches for Point Cloud Visualization

Ole Wegen

2024-10-17T14:39:00Z – 2024-10-17T14:51:00Z GMT-0600 Change your timezone on the schedule page

Interactive Reweighting for Mitigating Label Quality Issues

Weikai Yang

2024-10-17T14:51:00Z – 2024-10-17T15:03:00Z GMT-0600 Change your timezone on the schedule page

Graph Transformer for Label Placement

Jingwei Qu

2024-10-17T15:03:00Z – 2024-10-17T15:15:00Z GMT-0600 Change your timezone on the schedule page

DataGarden: Formalizing Personal Sketches into Structured Visualization Templates

Anna Offenwanger

2024-10-17T15:15:00Z – 2024-10-17T15:27:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Visualization Design Methods

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Miriah Meyer

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

"It's a Good Idea to Put It Into Words": Writing 'Rudders' in the Initial Stages of Visualization Design

Chase Stokes

2024-10-17T16:00:00Z – 2024-10-17T16:12:00Z GMT-0600 Change your timezone on the schedule page

Unveiling How Examples Shape Data Visualization Design Outcomes

Hannah K. Bako

2024-10-17T16:12:00Z – 2024-10-17T16:24:00Z GMT-0600 Change your timezone on the schedule page

Practices and Strategies in Responsive Thematic Map Design: A Report from Design Workshops with Experts

Sarah Schöttler

2024-10-17T16:24:00Z – 2024-10-17T16:36:00Z GMT-0600 Change your timezone on the schedule page

Path-based Design Model for Constructing and Exploring Alternative Visualisations

Jonathan C Roberts

2024-10-17T16:36:00Z – 2024-10-17T16:48:00Z GMT-0600 Change your timezone on the schedule page

Mind Drifts, Data Shifts: Utilizing Mind Wandering to Track the Evolution of User Experience with Data Visualizations

Anjana Arunkumar

2024-10-17T16:48:00Z – 2024-10-17T17:00:00Z GMT-0600 Change your timezone on the schedule page

Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks

Astrid van den Brandt

2024-10-17T17:00:00Z – 2024-10-17T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Journalism and Public Policy

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Sungahn Ko

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

More Than Data Stories: Broadening the Role of Visualization in Contemporary Journalism

Yu Fu

2024-10-17T17:45:00Z – 2024-10-17T17:57:00Z GMT-0600 Change your timezone on the schedule page

The Impact of Elicitation and Contrasting Narratives on Engagement, Recall and Attitude Change with News Articles Containing Data Visualization

Milad Rogha

2024-10-17T17:57:00Z – 2024-10-17T18:09:00Z GMT-0600 Change your timezone on the schedule page

What Can Interactive Visualization do for Participatory Budgeting in Chicago?

Alex Kale

2024-10-17T18:09:00Z – 2024-10-17T18:21:00Z GMT-0600 Change your timezone on the schedule page

The Backstory to “Swaying the Public”: A Design Chronicle of Election Forecast Visualizations

Fumeng Yang

2024-10-17T18:21:00Z – 2024-10-17T18:33:00Z GMT-0600 Change your timezone on the schedule page

Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration

Jinrui Wang

2024-10-17T18:33:00Z – 2024-10-17T18:45:00Z GMT-0600 Change your timezone on the schedule page

Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy

Xumeng Wang

2024-10-17T18:45:00Z – 2024-10-17T18:57:00Z GMT-0600 Change your timezone on the schedule page
IEEE VIS 2024 Content: Conference room - Bayshore V

Bayshore V

Current Session

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1st Workshop on Accessible Data Visualization

https://ieeevis.org/year/2024/program/event_w-accessible.html

Session chair: Brianna Wimer, Laura South

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Explaining Unfamiliar Genomics Data Visualizations to a Blind Individual through Transitions

Thomas C. Smits

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

A Screen reader and Sonifcation Approach for non-sighted Users to explore Data Visualizations on the Internet

Mandy Keck

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Toward Understanding the Experiences of People in Late Adulthood with Embedded Information Displays in the Home

Zack While

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

From Sight to Touch: Designing Tactile Data Physicalizations for Non-sighted Users

Mandy Keck

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Accessible SVG Charts with AChart

Keith Andrews

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Accessible Text Descriptions for UpSet Plots

Ishrat Jahan Eliza

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Using OpenKeyNav to Enhance the Keyboard-Accessibility of Web-based Data Visualization Tools

Lawrence Weru

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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Bio+MedVis Challenges: Bio+Med+Vis Workshop

https://ieeevis.org/year/2024/program/event_a-biomedchallenge.html

Session chair: Barbora Kozlikova, Nils Gehlenborg, Laura Garrison, Eric Mörth, Morgan Turner, Simon Warchol

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

TissuePlot: A Multi-Scale Interactive Web App For Visualizing Spatial Data

Heba Zuhair Sailem

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visual Compositional Data Analytics for Spatial Transcriptomics

David Hägele

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

A Simplified Positional Cell Type Visualization using Spatially Aggregated Clusters

Lee Mason

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

LLM - Supported Exploration of 3D Microscopy Imaging

Aarti Darji

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Droplets: A Marker Design for visually enhancing Local Cluster Association

Stefan Lengauer

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

A Part-to-Whole Circular Cell Explorer

Siyuan Zhao

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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SciVis Contest

https://ieeevis.org/year/2024/program/event_a-scivis-contest.html

Session chair: Karen Bemis, Tim Gerrits

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Current Session

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Enabling Scientific Discovery: A Tutorial for Harnessing the Power of the National Science Data Fabric for Large-Scale Data Analysis

https://ieeevis.org/year/2024/program/event_t-nationalscience.html

Session chair: Amy Gooch

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Current Session

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VIS Full Papers: Text, Annotation, and Metaphor

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Melanie Tory

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

The Role of Text in Visualizations: How Annotations Shape Perceptions of Bias and Influence Predictions

Chase Stokes

2024-10-16T12:30:00Z – 2024-10-16T12:42:00Z GMT-0600 Change your timezone on the schedule page

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space

Md Dilshadur Rahman

2024-10-16T12:42:00Z – 2024-10-16T12:54:00Z GMT-0600 Change your timezone on the schedule page

The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling

Hana Pokojná

2024-10-16T12:54:00Z – 2024-10-16T13:06:00Z GMT-0600 Change your timezone on the schedule page

From Instruction to Insight: Exploring the Semantic and Functional Roles of Text in Interactive Dashboards

Nicole Sultanum

2024-10-16T13:06:00Z – 2024-10-16T13:18:00Z GMT-0600 Change your timezone on the schedule page

"I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's Perspective

Xingyu Lan

2024-10-16T13:18:00Z – 2024-10-16T13:30:00Z GMT-0600 Change your timezone on the schedule page

CataAnno: An Ancient Catalog Annotator for Annotation Cleaning by Recommendation

Hanning Shao

2024-10-16T13:30:00Z – 2024-10-16T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Dimensionality Reduction

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Jian Zhao

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization

Markus Wallinger

2024-10-16T14:15:00Z – 2024-10-16T14:27:00Z GMT-0600 Change your timezone on the schedule page

Interpreting High-Dimensional Projections With Capacity

Siming Chen

2024-10-16T14:27:00Z – 2024-10-16T14:39:00Z GMT-0600 Change your timezone on the schedule page

DimBridge: Interactive Explanation of Visual Patterns in Dimensionality Reductions with Predicate Logic

Brian Montambault

2024-10-16T14:39:00Z – 2024-10-16T14:51:00Z GMT-0600 Change your timezone on the schedule page

2D Embeddings of Multi-dimensional Partitionings

Marina Evers

2024-10-16T14:51:00Z – 2024-10-16T15:03:00Z GMT-0600 Change your timezone on the schedule page

Accelerating hyperbolic t-SNE

Martin Skrodzki

2024-10-16T15:03:00Z – 2024-10-16T15:15:00Z GMT-0600 Change your timezone on the schedule page

TopoMap++: A faster and more space efficient technique to compute projections with topological guarantees

Vitoria Guardieiro

2024-10-16T15:15:00Z – 2024-10-16T15:27:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Collaboration and Communication

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Vidya Setlur

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions

Zixin Chen

2024-10-16T16:00:00Z – 2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction

Haoran Jiang

2024-10-16T16:12:00Z – 2024-10-16T16:24:00Z GMT-0600 Change your timezone on the schedule page

V-Mail: 3D-Enabled Correspondence about Spatial Data on (Almost) All Your Devices

Daniel F. Keefe

2024-10-16T16:24:00Z – 2024-10-16T16:36:00Z GMT-0600 Change your timezone on the schedule page

A Deixis-Centered Approach for Documenting Remote Synchronous Communication around Data Visualizations

Chang Han

2024-10-16T16:36:00Z – 2024-10-16T16:48:00Z GMT-0600 Change your timezone on the schedule page

Eliciting Multimodal and Collaborative Interactions for Data Exploration on Large Vertical Displays

Gabriela Molina León

2024-10-16T16:48:00Z – 2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page

Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics

Gabriela Molina León

2024-10-16T17:00:00Z – 2024-10-16T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Scripts, Notebooks, and Provenance

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Alex Lex

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Charting EDA: How Visualizations and Interactions Shape Analysis in Computational Notebooks.

Dylan Wootton

2024-10-16T17:45:00Z – 2024-10-16T17:57:00Z GMT-0600 Change your timezone on the schedule page

Ferry: Toward Better Understanding of Input/Output Space for Data Wrangling Scripts

Zhongsu Luo

2024-10-16T17:57:00Z – 2024-10-16T18:09:00Z GMT-0600 Change your timezone on the schedule page

Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks

Klaus Eckelt

2024-10-16T18:09:00Z – 2024-10-16T18:21:00Z GMT-0600 Change your timezone on the schedule page

Design Concerns for Integrated Scripting and Interactive Visualization in Notebook Environments

Connor Scully-Allison

2024-10-16T18:21:00Z – 2024-10-16T18:33:00Z GMT-0600 Change your timezone on the schedule page

Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics

Gustavo Moreira

2024-10-16T18:33:00Z – 2024-10-16T18:45:00Z GMT-0600 Change your timezone on the schedule page

ProvenanceWidgets: A Library of UI Control Elements to Track and Dynamically Overlay Analytic Provenance

Arpit Narechania

2024-10-16T18:45:00Z – 2024-10-16T18:57:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Model-checking and Validation

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Michael Correll

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis

Arran Zeyu Wang

2024-10-17T12:30:00Z – 2024-10-17T12:42:00Z GMT-0600 Change your timezone on the schedule page

Beware of Validation by Eye: Visual Validation of Linear Trends in Scatterplots

Daniel Braun

2024-10-17T12:42:00Z – 2024-10-17T12:54:00Z GMT-0600 Change your timezone on the schedule page

VMC: A Grammar for Visualizing Statistical Model Checks

Ziyang Guo

2024-10-17T12:54:00Z – 2024-10-17T13:06:00Z GMT-0600 Change your timezone on the schedule page

Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming on the Example of Cell Lineage

Jiayi Hong

2024-10-17T13:06:00Z – 2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page

Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments

Angie Boggust

2024-10-17T13:18:00Z – 2024-10-17T13:30:00Z GMT-0600 Change your timezone on the schedule page

ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics

Fan Yang

2024-10-17T13:30:00Z – 2024-10-17T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Applications: Sports. Games, and Finance

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Marc Streit

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting

Anqi Cao

2024-10-17T14:15:00Z – 2024-10-17T14:27:00Z GMT-0600 Change your timezone on the schedule page

Sportify: Question Answering with Embedded Visualizations and Personified Narratives for Sports Video

Chunggi Lee

2024-10-17T14:27:00Z – 2024-10-17T14:39:00Z GMT-0600 Change your timezone on the schedule page

Smartboard: Visual Exploration of Team Tactics with LLM Agent

Ziao Liu

2024-10-17T14:39:00Z – 2024-10-17T14:51:00Z GMT-0600 Change your timezone on the schedule page

FMLens: Towards Better Scaffolding the Process of Fund Manager Selection in Fund Investments

Longfei Chen

2024-10-17T14:51:00Z – 2024-10-17T15:03:00Z GMT-0600 Change your timezone on the schedule page

Tracing NFT Impact Dynamics in Transaction-flow Substitutive Systems with Visual Analytics

Yifan Cao

2024-10-17T15:03:00Z – 2024-10-17T15:15:00Z GMT-0600 Change your timezone on the schedule page

Who Let the Guards Out: Visual Support for Patrolling Games

Matěj Lang

2024-10-17T15:15:00Z – 2024-10-17T15:27:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Once Upon a Visualization

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Marti Hearst

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

DeLVE into Earth’s Past: A Visualization-Based Exhibit Deployed Across Multiple Museum Contexts

Mara Solen

2024-10-17T16:00:00Z – 2024-10-17T16:12:00Z GMT-0600 Change your timezone on the schedule page

Telling Data Stories with the Hero’s Journey: Design Guidance for Creating Data Videos

Zheng Wei

2024-10-17T16:12:00Z – 2024-10-17T16:24:00Z GMT-0600 Change your timezone on the schedule page

VisTellAR: Embedding Data Visualization to Short-form Videos Using Mobile Augmented Reality

Wai Tong

2024-10-17T16:24:00Z – 2024-10-17T16:36:00Z GMT-0600 Change your timezone on the schedule page

WonderFlow: Narration-Centric Design of Animated Data Videos

Leixian Shen

2024-10-17T16:36:00Z – 2024-10-17T16:48:00Z GMT-0600 Change your timezone on the schedule page

Reviving Static Charts into Live Charts

Lu Ying

2024-10-17T16:48:00Z – 2024-10-17T17:00:00Z GMT-0600 Change your timezone on the schedule page

DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs

Joohee Kim

2024-10-17T17:00:00Z – 2024-10-17T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Applications: Industry, Computing, and Medicine

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Joern Kohlhammer

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Visual Exploratory Analysis for Designing Large-Scale Network-on-Chip Architectures: A Domain Expert-Led Design Study

Yifan Sun

2024-10-17T17:45:00Z – 2024-10-17T17:57:00Z GMT-0600 Change your timezone on the schedule page

QuantumEyes: Towards Better Interpretability of Quantum Circuits

Shaolun Ruan

2024-10-17T17:57:00Z – 2024-10-17T18:09:00Z GMT-0600 Change your timezone on the schedule page

Bimodal Visualization of Industrial X-ray and Neutron Computed Tomography Data

Xuan Huang

2024-10-17T18:09:00Z – 2024-10-17T18:21:00Z GMT-0600 Change your timezone on the schedule page

Interactive Design-of-Experiments: Optimizing a Cooling System

Rainer Splechtna

2024-10-17T18:21:00Z – 2024-10-17T18:33:00Z GMT-0600 Change your timezone on the schedule page

DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing - A Design Study

Gabriela Morgenshterm

2024-10-17T18:33:00Z – 2024-10-17T18:45:00Z GMT-0600 Change your timezone on the schedule page

DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer

Andrew Wentzel

2024-10-17T18:45:00Z – 2024-10-17T18:57:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Look, Learn, Language Models

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Nicole Sultanum

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow

Dazhen Deng

2024-10-18T12:30:00Z – 2024-10-18T12:42:00Z GMT-0600 Change your timezone on the schedule page

LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models

Minsuk Kahng

2024-10-18T12:42:00Z – 2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page

Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

Lin Gao

2024-10-18T12:54:00Z – 2024-10-18T13:06:00Z GMT-0600 Change your timezone on the schedule page

Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning

Xingchen Zeng

2024-10-18T13:06:00Z – 2024-10-18T13:18:00Z GMT-0600 Change your timezone on the schedule page

How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts

Huichen Will Wang

2024-10-18T13:18:00Z – 2024-10-18T13:30:00Z GMT-0600 Change your timezone on the schedule page

Guided Health-related Information Seeking from LLMs via Knowledge Graph Integration

Qianwen Wang

2024-10-18T13:30:00Z – 2024-10-18T13:42:00Z GMT-0600 Change your timezone on the schedule page
IEEE VIS 2024 Content: Conference room - Bayshore V

Bayshore V

Current Session

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1st Workshop on Accessible Data Visualization

https://ieeevis.org/year/2024/program/event_w-accessible.html

Session chair: Brianna Wimer, Laura South

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Explaining Unfamiliar Genomics Data Visualizations to a Blind Individual through Transitions

Thomas C. Smits

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

A Screen reader and Sonifcation Approach for non-sighted Users to explore Data Visualizations on the Internet

Mandy Keck

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Toward Understanding the Experiences of People in Late Adulthood with Embedded Information Displays in the Home

Zack While

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

From Sight to Touch: Designing Tactile Data Physicalizations for Non-sighted Users

Mandy Keck

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Accessible SVG Charts with AChart

Keith Andrews

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Accessible Text Descriptions for UpSet Plots

Ishrat Jahan Eliza

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Using OpenKeyNav to Enhance the Keyboard-Accessibility of Web-based Data Visualization Tools

Lawrence Weru

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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Bio+MedVis Challenges: Bio+Med+Vis Workshop

https://ieeevis.org/year/2024/program/event_a-biomedchallenge.html

Session chair: Barbora Kozlikova, Nils Gehlenborg, Laura Garrison, Eric Mörth, Morgan Turner, Simon Warchol

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

TissuePlot: A Multi-Scale Interactive Web App For Visualizing Spatial Data

Heba Zuhair Sailem

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visual Compositional Data Analytics for Spatial Transcriptomics

David Hägele

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

A Simplified Positional Cell Type Visualization using Spatially Aggregated Clusters

Lee Mason

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

LLM - Supported Exploration of 3D Microscopy Imaging

Aarti Darji

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Droplets: A Marker Design for visually enhancing Local Cluster Association

Stefan Lengauer

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

A Part-to-Whole Circular Cell Explorer

Siyuan Zhao

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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SciVis Contest

https://ieeevis.org/year/2024/program/event_a-scivis-contest.html

Session chair: Karen Bemis, Tim Gerrits

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

PlumeViz: Interactive Exploration for Multi-Facet Features of Hydrothermal Plumes in Sonar Images

Yiming Shao

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visualization of Sonar Imaging for Hydrothermal Systems

Tran Nguyen Anh Minh

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Topology Based Visualization of Hydrothermal Plumes

Harikrishnan Pattathil

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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Enabling Scientific Discovery: A Tutorial for Harnessing the Power of the National Science Data Fabric for Large-Scale Data Analysis

https://ieeevis.org/year/2024/program/event_t-nationalscience.html

Session chair: Amy Gooch

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Current Session

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VIS Full Papers: Text, Annotation, and Metaphor

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Melanie Tory

2024-10-16T12:30:00Z – 2024-10-16T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

The Role of Text in Visualizations: How Annotations Shape Perceptions of Bias and Influence Predictions

Chase Stokes

2024-10-16T12:30:00Z – 2024-10-16T12:42:00Z GMT-0600 Change your timezone on the schedule page

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space

Md Dilshadur Rahman

2024-10-16T12:42:00Z – 2024-10-16T12:54:00Z GMT-0600 Change your timezone on the schedule page

The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling

Hana Pokojná

2024-10-16T12:54:00Z – 2024-10-16T13:06:00Z GMT-0600 Change your timezone on the schedule page

From Instruction to Insight: Exploring the Semantic and Functional Roles of Text in Interactive Dashboards

Nicole Sultanum

2024-10-16T13:06:00Z – 2024-10-16T13:18:00Z GMT-0600 Change your timezone on the schedule page

"I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's Perspective

Xingyu Lan

2024-10-16T13:18:00Z – 2024-10-16T13:30:00Z GMT-0600 Change your timezone on the schedule page

CataAnno: An Ancient Catalog Annotator for Annotation Cleaning by Recommendation

Hanning Shao

2024-10-16T13:30:00Z – 2024-10-16T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Dimensionality Reduction

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Jian Zhao

2024-10-16T14:15:00Z – 2024-10-16T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization

Markus Wallinger

2024-10-16T14:15:00Z – 2024-10-16T14:27:00Z GMT-0600 Change your timezone on the schedule page

Interpreting High-Dimensional Projections With Capacity

Siming Chen

2024-10-16T14:27:00Z – 2024-10-16T14:39:00Z GMT-0600 Change your timezone on the schedule page

DimBridge: Interactive Explanation of Visual Patterns in Dimensionality Reductions with Predicate Logic

Brian Montambault

2024-10-16T14:39:00Z – 2024-10-16T14:51:00Z GMT-0600 Change your timezone on the schedule page

2D Embeddings of Multi-dimensional Partitionings

Marina Evers

2024-10-16T14:51:00Z – 2024-10-16T15:03:00Z GMT-0600 Change your timezone on the schedule page

Accelerating hyperbolic t-SNE

Martin Skrodzki

2024-10-16T15:03:00Z – 2024-10-16T15:15:00Z GMT-0600 Change your timezone on the schedule page

TopoMap++: A faster and more space efficient technique to compute projections with topological guarantees

Vitoria Guardieiro

2024-10-16T15:15:00Z – 2024-10-16T15:27:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Collaboration and Communication

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Vidya Setlur

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions

Zixin Chen

2024-10-16T16:00:00Z – 2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction

Haoran Jiang

2024-10-16T16:12:00Z – 2024-10-16T16:24:00Z GMT-0600 Change your timezone on the schedule page

V-Mail: 3D-Enabled Correspondence about Spatial Data on (Almost) All Your Devices

Daniel F. Keefe

2024-10-16T16:24:00Z – 2024-10-16T16:36:00Z GMT-0600 Change your timezone on the schedule page

A Deixis-Centered Approach for Documenting Remote Synchronous Communication around Data Visualizations

Chang Han

2024-10-16T16:36:00Z – 2024-10-16T16:48:00Z GMT-0600 Change your timezone on the schedule page

Eliciting Multimodal and Collaborative Interactions for Data Exploration on Large Vertical Displays

Gabriela Molina León

2024-10-16T16:48:00Z – 2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page

Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics

Gabriela Molina León

2024-10-16T17:00:00Z – 2024-10-16T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Scripts, Notebooks, and Provenance

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Alex Lex

2024-10-16T17:45:00Z – 2024-10-16T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Charting EDA: How Visualizations and Interactions Shape Analysis in Computational Notebooks.

Dylan Wootton

2024-10-16T17:45:00Z – 2024-10-16T17:57:00Z GMT-0600 Change your timezone on the schedule page

Ferry: Toward Better Understanding of Input/Output Space for Data Wrangling Scripts

Zhongsu Luo

2024-10-16T17:57:00Z – 2024-10-16T18:09:00Z GMT-0600 Change your timezone on the schedule page

Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks

Klaus Eckelt

2024-10-16T18:09:00Z – 2024-10-16T18:21:00Z GMT-0600 Change your timezone on the schedule page

Design Concerns for Integrated Scripting and Interactive Visualization in Notebook Environments

Connor Scully-Allison

2024-10-16T18:21:00Z – 2024-10-16T18:33:00Z GMT-0600 Change your timezone on the schedule page

Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics

Gustavo Moreira

2024-10-16T18:33:00Z – 2024-10-16T18:45:00Z GMT-0600 Change your timezone on the schedule page

ProvenanceWidgets: A Library of UI Control Elements to Track and Dynamically Overlay Analytic Provenance

Arpit Narechania

2024-10-16T18:45:00Z – 2024-10-16T18:57:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Model-checking and Validation

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Michael Correll

2024-10-17T12:30:00Z – 2024-10-17T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis

Arran Zeyu Wang

2024-10-17T12:30:00Z – 2024-10-17T12:42:00Z GMT-0600 Change your timezone on the schedule page

Beware of Validation by Eye: Visual Validation of Linear Trends in Scatterplots

Daniel Braun

2024-10-17T12:42:00Z – 2024-10-17T12:54:00Z GMT-0600 Change your timezone on the schedule page

VMC: A Grammar for Visualizing Statistical Model Checks

Ziyang Guo

2024-10-17T12:54:00Z – 2024-10-17T13:06:00Z GMT-0600 Change your timezone on the schedule page

Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming on the Example of Cell Lineage

Jiayi Hong

2024-10-17T13:06:00Z – 2024-10-17T13:18:00Z GMT-0600 Change your timezone on the schedule page

Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments

Angie Boggust

2024-10-17T13:18:00Z – 2024-10-17T13:30:00Z GMT-0600 Change your timezone on the schedule page

ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics

Fan Yang

2024-10-17T13:30:00Z – 2024-10-17T13:42:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Applications: Sports. Games, and Finance

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Marc Streit

2024-10-17T14:15:00Z – 2024-10-17T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting

Anqi Cao

2024-10-17T14:15:00Z – 2024-10-17T14:27:00Z GMT-0600 Change your timezone on the schedule page

Sportify: Question Answering with Embedded Visualizations and Personified Narratives for Sports Video

Chunggi Lee

2024-10-17T14:27:00Z – 2024-10-17T14:39:00Z GMT-0600 Change your timezone on the schedule page

Smartboard: Visual Exploration of Team Tactics with LLM Agent

Ziao Liu

2024-10-17T14:39:00Z – 2024-10-17T14:51:00Z GMT-0600 Change your timezone on the schedule page

FMLens: Towards Better Scaffolding the Process of Fund Manager Selection in Fund Investments

Longfei Chen

2024-10-17T14:51:00Z – 2024-10-17T15:03:00Z GMT-0600 Change your timezone on the schedule page

Tracing NFT Impact Dynamics in Transaction-flow Substitutive Systems with Visual Analytics

Yifan Cao

2024-10-17T15:03:00Z – 2024-10-17T15:15:00Z GMT-0600 Change your timezone on the schedule page

Who Let the Guards Out: Visual Support for Patrolling Games

Matěj Lang

2024-10-17T15:15:00Z – 2024-10-17T15:27:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Once Upon a Visualization

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Marti Hearst

2024-10-17T16:00:00Z – 2024-10-17T17:15:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

DeLVE into Earth’s Past: A Visualization-Based Exhibit Deployed Across Multiple Museum Contexts

Mara Solen

2024-10-17T16:00:00Z – 2024-10-17T16:12:00Z GMT-0600 Change your timezone on the schedule page

Telling Data Stories with the Hero’s Journey: Design Guidance for Creating Data Videos

Zheng Wei

2024-10-17T16:12:00Z – 2024-10-17T16:24:00Z GMT-0600 Change your timezone on the schedule page

VisTellAR: Embedding Data Visualization to Short-form Videos Using Mobile Augmented Reality

Wai Tong

2024-10-17T16:24:00Z – 2024-10-17T16:36:00Z GMT-0600 Change your timezone on the schedule page

WonderFlow: Narration-Centric Design of Animated Data Videos

Leixian Shen

2024-10-17T16:36:00Z – 2024-10-17T16:48:00Z GMT-0600 Change your timezone on the schedule page

Reviving Static Charts into Live Charts

Lu Ying

2024-10-17T16:48:00Z – 2024-10-17T17:00:00Z GMT-0600 Change your timezone on the schedule page

DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs

Joohee Kim

2024-10-17T17:00:00Z – 2024-10-17T17:12:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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VIS Full Papers: Applications: Industry, Computing, and Medicine

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Joern Kohlhammer

2024-10-17T17:45:00Z – 2024-10-17T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Visual Exploratory Analysis for Designing Large-Scale Network-on-Chip Architectures: A Domain Expert-Led Design Study

Yifan Sun

2024-10-17T17:45:00Z – 2024-10-17T17:57:00Z GMT-0600 Change your timezone on the schedule page

QuantumEyes: Towards Better Interpretability of Quantum Circuits

Shaolun Ruan

2024-10-17T17:57:00Z – 2024-10-17T18:09:00Z GMT-0600 Change your timezone on the schedule page

Bimodal Visualization of Industrial X-ray and Neutron Computed Tomography Data

Xuan Huang

2024-10-17T18:09:00Z – 2024-10-17T18:21:00Z GMT-0600 Change your timezone on the schedule page

Interactive Design-of-Experiments: Optimizing a Cooling System

Rainer Splechtna

2024-10-17T18:21:00Z – 2024-10-17T18:33:00Z GMT-0600 Change your timezone on the schedule page

DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing - A Design Study

Gabriela Morgenshterm

2024-10-17T18:33:00Z – 2024-10-17T18:45:00Z GMT-0600 Change your timezone on the schedule page

DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer

Andrew Wentzel

2024-10-17T18:45:00Z – 2024-10-17T18:57:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VIS Full Papers: Look, Learn, Language Models

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Nicole Sultanum

2024-10-18T12:30:00Z – 2024-10-18T13:45:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow

Dazhen Deng

2024-10-18T12:30:00Z – 2024-10-18T12:42:00Z GMT-0600 Change your timezone on the schedule page

LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models

Minsuk Kahng

2024-10-18T12:42:00Z – 2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page

Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

Lin Gao

2024-10-18T12:54:00Z – 2024-10-18T13:06:00Z GMT-0600 Change your timezone on the schedule page

Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning

Xingchen Zeng

2024-10-18T13:06:00Z – 2024-10-18T13:18:00Z GMT-0600 Change your timezone on the schedule page

How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts

Huichen Will Wang

2024-10-18T13:18:00Z – 2024-10-18T13:30:00Z GMT-0600 Change your timezone on the schedule page

Guided Health-related Information Seeking from LLMs via Knowledge Graph Integration

Qianwen Wang

2024-10-18T13:30:00Z – 2024-10-18T13:42:00Z GMT-0600 Change your timezone on the schedule page
IEEE VIS 2024 Content: Conference room - Esplanade Suites I + II + III

Esplanade Suites I + II + III

Current Session

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EduVis: Workshop on Visualization Education, Literacy, and Activities: EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

https://ieeevis.org/year/2024/program/event_w-eduvis.html

Session chair: Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Workshop Opening & Outline

2024-10-13T12:30:00Z – 2024-10-13T12:40:00Z GMT-0600 Change your timezone on the schedule page

Keynote

2024-10-13T12:40:00Z – 2024-10-13T13:10:00Z GMT-0600 Change your timezone on the schedule page

Papers Presentations and Discussion

2024-10-13T13:10:00Z – 2024-10-13T13:45:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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EduVis: Workshop on Visualization Education, Literacy, and Activities: EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 2)

https://ieeevis.org/year/2024/program/event_w-eduvis.html

Session chair: Jillian Aurisano, Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Papers Presentations and Discussion

2024-10-13T14:15:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Papers Presentations and Discussion

2024-10-13T16:00:00Z – 2024-10-13T17:15:00Z GMT-0600 Change your timezone on the schedule page

Discussion: Integrating AI in Data Visualization Education

2024-10-13T17:45:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

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Visualization for Climate Action and Sustainability

https://ieeevis.org/year/2024/program/event_w-vis4climate.html

Session chair: Benjamin Bach, Fanny Chevalier, Helen-Nicole Kostis, Mark SubbaRao, Yvonne Jansen, Robert Soden

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Local Climate Data Stories: Data-driven Storytelling to Communicate Effects and Mitigation of Climate Change in a Local Context

Fabian Beck

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

EcoViz: an iterative methodology for designing multifaceted data-driven environmental visualizations that communicate ecosystem impacts and envision nature-based solutions

Jessica Marielle Kendall-Bar

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Eco-Garden: A Data Sculpture to Encourage Sustainable Practices in Everyday Life in Households

Dushani Ushettige

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

AwARe: Using handheld augmented reality for researching the potential of food resource information visualization

Nina Rosa

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Cultivating Climate Action Through Multi-Institutional Collaboration: Innovative Data Visualization Educational Programs and Exhibits for Public Engagement

Beth Altringer Eagle

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Harnessing Visualization for Climate Action and Sustainable Future

Narges Mahyar

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Earth Mission Control: Advanced Data Visualizations for Climate Intelligence

Rachel Connolly

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Artists, Data and Climate Change: Distilled messages, multiple entry points, layered metaphor

Francesca Samsel

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Interactive Visualization of Ensemble Data Assimilation Forecasts for Freshwater Floods

Ameya B Patil

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Data Comics for Climate Change

Zezhong Wang

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Urban Computing for Climate And Environmental Justice: Early Perspectives From Two Research Initiatives

Fabio Miranda

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Designing Visualizations for Enhancing Carbon Numeracy

Katerina Batziakoudi

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Exploring the Reproducibility for Visualization Figures in Climate Change Report

Lu Ying

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VISions of the Future: Workshop on Sustainable Practices within Visualization and Physicalisation

https://ieeevis.org/year/2024/program/event_w-future.html

Session chair: Georgia Panagiotidou, Luiz Morais, Sarah Hayes, Derya Akbaba, Tatiana Losev, Andrew McNutt

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Rain Gauge: Exploring the Design and Sustainability of 3D Printed Clay Physicalizations

Bridger Herman

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

(Almost) All Data is Absent Data

Karly Ross

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Renewable Energy Data Visualization: A study with Open Data

Gustavo Santos Silva

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Reimagining Data Visualization to Address Sustainability Goals

Narges Mahyar

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visual and Data Journalism as Tools for Fighting Climate Change

Emilly Brito

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page
IEEE VIS 2024 Content: Conference room - Esplanade Suites I + II + III

Esplanade Suites I + II + III

Current Session

Next Session

EduVis: Workshop on Visualization Education, Literacy, and Activities: EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

https://ieeevis.org/year/2024/program/event_w-eduvis.html

Session chair: Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Workshop Opening & Outline

2024-10-13T12:30:00Z – 2024-10-13T12:40:00Z GMT-0600 Change your timezone on the schedule page

Keynote

2024-10-13T12:40:00Z – 2024-10-13T13:10:00Z GMT-0600 Change your timezone on the schedule page

Papers Presentations and Discussion

2024-10-13T13:10:00Z – 2024-10-13T13:45:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

EduVis: Workshop on Visualization Education, Literacy, and Activities: EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 2)

https://ieeevis.org/year/2024/program/event_w-eduvis.html

Session chair: Jillian Aurisano, Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Papers Presentations and Discussion

2024-10-13T14:15:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Papers Presentations and Discussion

2024-10-13T16:00:00Z – 2024-10-13T17:15:00Z GMT-0600 Change your timezone on the schedule page

Discussion: Integrating AI in Data Visualization Education

2024-10-13T17:45:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

Visualization for Climate Action and Sustainability

https://ieeevis.org/year/2024/program/event_w-vis4climate.html

Session chair: Benjamin Bach, Fanny Chevalier, Helen-Nicole Kostis, Mark SubbaRao, Yvonne Jansen, Robert Soden

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Local Climate Data Stories: Data-driven Storytelling to Communicate Effects and Mitigation of Climate Change in a Local Context

Fabian Beck

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

EcoViz: an iterative methodology for designing multifaceted data-driven environmental visualizations that communicate ecosystem impacts and envision nature-based solutions

Jessica Marielle Kendall-Bar

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Eco-Garden: A Data Sculpture to Encourage Sustainable Practices in Everyday Life in Households

Dushani Ushettige

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

AwARe: Using handheld augmented reality for researching the potential of food resource information visualization

Nina Rosa

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Cultivating Climate Action Through Multi-Institutional Collaboration: Innovative Data Visualization Educational Programs and Exhibits for Public Engagement

Beth Altringer Eagle

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Harnessing Visualization for Climate Action and Sustainable Future

Narges Mahyar

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Earth Mission Control: Advanced Data Visualizations for Climate Intelligence

Rachel Connolly

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Artists, Data and Climate Change: Distilled messages, multiple entry points, layered metaphor

Francesca Samsel

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Interactive Visualization of Ensemble Data Assimilation Forecasts for Freshwater Floods

Ameya B Patil

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Data Comics for Climate Change

Zezhong Wang

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Urban Computing for Climate And Environmental Justice: Early Perspectives From Two Research Initiatives

Fabio Miranda

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Designing Visualizations for Enhancing Carbon Numeracy

Katerina Batziakoudi

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Exploring the Reproducibility for Visualization Figures in Climate Change Report

Lu Ying

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Current Session

Next Session

VISions of the Future: Workshop on Sustainable Practices within Visualization and Physicalisation

https://ieeevis.org/year/2024/program/event_w-future.html

Session chair: Georgia Panagiotidou, Luiz Morais, Sarah Hayes, Derya Akbaba, Tatiana Losev, Andrew McNutt

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

… in this session…

Rain Gauge: Exploring the Design and Sustainability of 3D Printed Clay Physicalizations

Bridger Herman

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

(Almost) All Data is Absent Data

Karly Ross

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Renewable Energy Data Visualization: A study with Open Data

Gustavo Santos Silva

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Reimagining Data Visualization to Address Sustainability Goals

Narges Mahyar

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visual and Data Journalism as Tools for Fighting Climate Change

Emilly Brito

2024-10-14T16:00:00Z – 2024-10-14T19:00:00Z GMT-0600 Change your timezone on the schedule page
IEEE VIS 2024 Content: VDS: Visualization in Data Science Symposium

VDS: Visualization in Data Science Symposium

https://ieeevis.org/year/2024/program/event_s-vds.html

Session chair: Ana Crisan, Dylan Cashman

Room: Bayshore I

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Jump to event listing

Presentations in this session:

Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent

Authors: Yannick Metz, Dennis Ackermann, Daniel Keim, Maximilian T. Fischer

Maximilian T. Fischer

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visualization and Automation in Data Science: Exploring the Paradox of Humans-in-the-Loop

Authors: Jen Rogers, Mehdi Chakhchoukh, Marie Anastacio, Rebecca Faust, Cagatay Turkay, Lars Kotthoff, Steffen Koch, Andreas Kerren, Jürgen Bernard

Jen Rogers

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

The Categorical Data Map: A Multidimensional Scaling-Based Approach

Authors: Frederik L. Dennig, Lucas Joos, Patrick Paetzold, Daniela Blumberg, Oliver Deussen, Daniel Keim, Maximilian T. Fischer

Frederik L. Dennig

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Towards a Visual Perception-Based Analysis of Clustering Quality Metrics

Authors: Graziano Blasilli, Daniel Kerrigan, Enrico Bertini, Giuseppe Santucci

Graziano Blasilli

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems

Authors: Yongsu Ahn, Quinn K Wolter, Jonilyn Dick, Janet Dick, Yu-Ru Lin

Yongsu Ahn

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Seeing the Shift: Keep an Eye on Semantic Changes in Times of LLMs

Authors: Raphael Buchmüller, Friederike Körte, Daniel Keim

Raphael Buchmüller

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: VDS: Visualization in Data Science Symposium

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.

IEEE VIS 2024 Content: VDS: Visualization in Data Science Symposium

VDS: Visualization in Data Science Symposium

https://ieeevis.org/year/2024/program/event_s-vds.html

Session chair: Ana Crisan, Dylan Cashman, Saugat Pandey, Alvitta Ottley, John E Wenskovitch

Room: Bayshore I

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page
2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z

Jump to event listing

Presentations in this session:

Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent

Authors: Yannick Metz, Dennis Ackermann, Daniel Keim, Maximilian T. Fischer

Maximilian T. Fischer

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Visualization and Automation in Data Science: Exploring the Paradox of Humans-in-the-Loop

Authors: Jen Rogers, Mehdi Chakhchoukh, Marie Anastacio, Rebecca Faust, Cagatay Turkay, Lars Kotthoff, Steffen Koch, Andreas Kerren, Jürgen Bernard

Jen Rogers

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

The Categorical Data Map: A Multidimensional Scaling-Based Approach

Authors: Frederik L. Dennig, Lucas Joos, Patrick Paetzold, Daniela Blumberg, Oliver Deussen, Daniel Keim, Maximilian T. Fischer

Frederik L. Dennig

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Towards a Visual Perception-Based Analysis of Clustering Quality Metrics

Authors: Graziano Blasilli, Daniel Kerrigan, Enrico Bertini, Giuseppe Santucci

Graziano Blasilli

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems

Authors: Yongsu Ahn, Quinn K Wolter, Jonilyn Dick, Janet Dick, Yu-Ru Lin

Yongsu Ahn

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

Seeing the Shift: Keep an Eye on Semantic Changes in Times of LLMs

Authors: Raphael Buchmüller, Friederike Körte, Daniel Keim

Raphael Buchmüller

2024-10-13T16:00:00Z – 2024-10-13T19:00:00Z GMT-0600 Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: VDS: Visualization in Data Science Symposium

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.

IEEE VIS 2024 Content: VAST Challenge

VAST Challenge

https://ieeevis.org/year/2024/program/event_a-vast-challenge.html

Session chair: R. Jordan Crouser, Steve Gomez, Jereme Haack

Room: Bayshore II

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Jump to event listing

Presentations in this session:

Visual Analysis of Complex Temporal Networks Supported by Analytic Provenance

Authors: Yuhan Guo, Yuchu Luo, Xinyue Chen, Hanning Shao, Xiaoru Yuan, Kai Xu

Yuhan Guo

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Tracking Overfishing: Visual Analytics of Suspicious Behaviors in Commercial Fishing Vessels

Authors: Juanpablo Andrew Heredia, Fabrício Venturim, Dany Mauro Diaz Espino, Felipe Moreno-Vera, Jorge Poco

Juanpablo Andrew Heredia

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

LIST-Durant-MC1

Authors: Eloi Durant, Nicolas Medoc, Mohammad Ghoniem

Eloi Durant

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visual Anomaly Detection in Temporal Knowledge Graphs

Authors: Magdalena Allmann, Kevin Iselborn, Jan-Tobias Sohns, Heike Leitte

Kevin Iselborn

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

UKON-MC2-1

Authors: Lisa-Maria Reutlinger, Julian Jandeleit, Fred Kunze, Antonella Bidlingmaier, Tolga Tuncer, Udo Schlegel, Daniel Keim

Lisa-Maria Reutlinger

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

VAST 2024-MC2 Challenge

Authors: Sinem Bilge Guler, Mehmet Emre Sahin, Funda Yildiz-Aydin, Daniel Keim, Udo Schlegel

Sinem Bilge Guler

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

UKON-Buchmueller-MC1

Authors: Raphael Buchmüller, Daniel Fürst, Alexander Frings, Udo Schlegel, Daniel Keim

Daniel Fürst

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Purdue-Chen-MC2

Authors: Ashley Yang, Hao Wang, Qianlai Yang, Qi Yang, Ziqian Gong, Zizun Zhou, Zhenyu Cheryl Qian, Yingjie Victor Chen

Hao Wang

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

FishEye Watcher: a visual analytics system for knowledge graph bias detection

Authors: Tian Qiu, Yi Shan, Xueli Shu, Aolin Guo, Qianhui Li, Meng Guo, Siming Chen

Tian Qiu

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Utilizing TimeArcs and PageRank to Solve the 2024 VAST Challenge Mini Challenge 3

Authors: Ethan Wei, Tommy Dang

Ethan Wei

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

FishBiasLens: Integrating Large Language Models and Visual Analytics for Bias Detection

Authors: Dany Mauro Diaz Espino, Felipe Moreno-Vera, Juanpablo Andrew Heredia, Fabrício Venturim, Jorge Poco

Dany Mauro Diaz Espino

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visualization for Oceanus' Fishing Market

Authors: Xueli Shu, Qianhui Li, Yi Shan, Aolin Guo, Tian Qiu, Siming Chen, Meng Guo

Xueli Shu

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visual Analytics for Detecting Illegal Transport Activities

Authors: Yi Shan, Aolin Guo, Zekai Shao, Tian Qiu, Xueli Shu, Qianhui Li, Siming Chen

Yi Shan

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: VAST Challenge

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.

IEEE VIS 2024 Content: VAST Challenge

VAST Challenge

https://ieeevis.org/year/2024/program/event_a-vast-challenge.html

Session chair: R. Jordan Crouser, Steve Gomez, Jereme Haack

Room: Bayshore II

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Jump to event listing

Presentations in this session:

Visual Analysis of Complex Temporal Networks Supported by Analytic Provenance

Authors: Yuhan Guo, Yuchu Luo, Xinyue Chen, Hanning Shao, Xiaoru Yuan, Kai Xu

Yuhan Guo

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Tracking Overfishing: Visual Analytics of Suspicious Behaviors in Commercial Fishing Vessels

Authors: Juanpablo Andrew Heredia, Fabrício Venturim, Dany Mauro Diaz Espino, Felipe Moreno-Vera, Jorge Poco

Juanpablo Andrew Heredia

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visual Anomaly Detection in Temporal Knowledge Graphs

Authors: Magdalena Allmann, Kevin Iselborn, Jan-Tobias Sohns, Heike Leitte

Kevin Iselborn

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

VAST 2024-MC2 Challenge

Authors: Sinem Bilge Guler, Mehmet Emre Sahin, Funda Yildiz-Aydin, Daniel Keim, Udo Schlegel

Sinem Bilge Guler

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

UKON-Buchmueller-MC1

Authors: Raphael Buchmüller, Daniel Fürst, Alexander Frings, Udo Schlegel, Daniel Keim

Daniel Fürst

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Purdue-Chen-MC2

Authors: Ashley Yang, Hao Wang, Qianlai Yang, Qi Yang, Ziqian Gong, Zizun Zhou, Zhenyu Cheryl Qian, Yingjie Victor Chen

Hao Wang

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

FishEye Watcher: a visual analytics system for knowledge graph bias detection

Authors: Tian Qiu, Yi Shan, Xueli Shu, Aolin Guo, Qianhui Li, Meng Guo, Siming Chen

Tian Qiu

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Utilizing TimeArcs and PageRank to Solve the 2024 VAST Challenge Mini Challenge 3

Authors: Ethan Wei, Tommy Dang

Ethan Wei

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

FishBiasLens: Integrating Large Language Models and Visual Analytics for Bias Detection

Authors: Dany Mauro Diaz Espino, Felipe Moreno-Vera, Juanpablo Andrew Heredia, Fabrício Venturim, Jorge Poco

Dany Mauro Diaz Espino

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visual Analytics for Detecting Illegal Transport Activities

Authors: Yi Shan, Aolin Guo, Zekai Shao, Tian Qiu, Xueli Shu, Qianhui Li, Siming Chen

Yi Shan

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: VAST Challenge

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.

IEEE VIS 2024 Content: SciVis Contest

SciVis Contest

https://ieeevis.org/year/2024/program/event_a-scivis-contest.html

Session chair: Karen Bemis, Tim Gerrits

Room: Bayshore V

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

Jump to event listing

You may want to also jump to the parent event to see related presentations: SciVis Contest

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.

IEEE VIS 2024 Content: SciVis Contest

SciVis Contest

https://ieeevis.org/year/2024/program/event_a-scivis-contest.html

Session chair: Karen Bemis, Tim Gerrits

Room: Bayshore V

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z

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Presentations in this session:

PlumeViz: Interactive Exploration for Multi-Facet Features of Hydrothermal Plumes in Sonar Images

Authors: Yiming Shao, Chengming Liu, Zhiyuan Meng, Shufan Qian, Peng Jiang, Yunhai Wang, Dr. Qiong Zeng

Yiming Shao

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Visualization of Sonar Imaging for Hydrothermal Systems

Authors: Ngan V. T. Nguyen, Minh N. A. Tran, Si Chi Hoang, Vuong Tran Thien, Nguyen Tran Nguyen Thanh, Ngo Ly, Phuc Thien Nguyen, Sinh Huy Gip, Sang Thanh Ngo, Nguyễn Thái Hòa

Tran Nguyen Anh Minh

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

Topology Based Visualization of Hydrothermal Plumes

Authors: Adhitya Kamakshidasan, Harikrishnan Pattathil

Harikrishnan Pattathil

2024-10-14T12:30:00Z – 2024-10-14T15:30:00Z GMT-0600 Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: SciVis Contest

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.

IEEE VIS 2024 Content: VIS Full Papers: Perception and Cognition

VIS Full Papers: Perception and Cognition

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Bernhard Preim

Room: Bayshore II

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

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Presentations in this session:

The Impact of Vertical Scaling on Normal Probability Density Function Plots

Authors: Racquel Fygenson, Lace M. Padilla

Racquel Fygenson

2024-10-16T16:00:00Z – 2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page

The Effect of Visual Aids on Reading Numeric Data Tables

Authors: YongFeng Ji, Charles Perin, Miguel A Nacenta

Charles Perin

2024-10-16T16:12:00Z – 2024-10-16T16:24:00Z GMT-0600 Change your timezone on the schedule page

Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Authors: Carter Blair, Xiyao Wang, Charles Perin

Charles Perin

2024-10-16T16:24:00Z – 2024-10-16T16:36:00Z GMT-0600 Change your timezone on the schedule page

Examining Limits of Small Multiples: Frame Quantity Impacts Judgments with Line Graphs

Authors: Helia Hosseinpour, Laura E. Matzen, Kristin M. Divis, Spencer C. Castro, Lace Padilla

Helia Hosseinpour

2024-10-16T16:36:00Z – 2024-10-16T16:48:00Z GMT-0600 Change your timezone on the schedule page

Memory Recall for Data Visualizations in Mixed Reality, Virtual Reality, 3D, and 2D

Authors: Christophe Hurter, Bernice Rogowitz, Guillaume Truong, Tiffany Andry, Hugo Romat, Ludovic Gardy, Fereshteh Amini, Nathalie Henry Riche

Christophe Hurter

2024-10-16T16:48:00Z – 2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page

Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

Authors: Arvind Srinivasan, Johannes Ellemose, Peter W. S. Butcher, Panagiotis D. Ritsos, Niklas Elmqvist

Arvind Srinivasan , Johannes Ellemose

2024-10-16T17:00:00Z – 2024-10-16T17:12:00Z GMT-0600 Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: VIS Full Papers

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.

IEEE VIS 2024 Content: VIS Full Papers: Perception and Cognition

VIS Full Papers: Perception and Cognition

https://ieeevis.org/year/2024/program/event_v-full.html

Session chair: Tamara Munzner

Room: Bayshore II

2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T16:00:00Z – 2024-10-16T17:15:00Z

Jump to event listing

Presentations in this session:

The Impact of Vertical Scaling on Normal Probability Density Function Plots

Authors: Racquel Fygenson, Lace M. Padilla

Racquel Fygenson

2024-10-16T16:00:00Z – 2024-10-16T16:12:00Z GMT-0600 Change your timezone on the schedule page

The Effect of Visual Aids on Reading Numeric Data Tables

Authors: YongFeng Ji, Charles Perin, Miguel A Nacenta

Charles Perin

2024-10-16T16:12:00Z – 2024-10-16T16:24:00Z GMT-0600 Change your timezone on the schedule page

Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Authors: Carter Blair, Xiyao Wang, Charles Perin

Charles Perin

2024-10-16T16:24:00Z – 2024-10-16T16:36:00Z GMT-0600 Change your timezone on the schedule page

Examining Limits of Small Multiples: Frame Quantity Impacts Judgments with Line Graphs

Authors: Helia Hosseinpour, Laura E. Matzen, Kristin M. Divis, Spencer C. Castro, Lace Padilla

Helia Hosseinpour

2024-10-16T16:36:00Z – 2024-10-16T16:48:00Z GMT-0600 Change your timezone on the schedule page

Memory Recall for Data Visualizations in Mixed Reality, Virtual Reality, 3D, and 2D

Authors: Christophe Hurter, Bernice Rogowitz, Guillaume Truong, Tiffany Andry, Hugo Romat, Ludovic Gardy, Fereshteh Amini, Nathalie Henry Riche

Christophe Hurter

2024-10-16T16:48:00Z – 2024-10-16T17:00:00Z GMT-0600 Change your timezone on the schedule page

Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

Authors: Arvind Srinivasan, Johannes Ellemose, Peter W. S. Butcher, Panagiotis D. Ritsos, Niklas Elmqvist

Arvind Srinivasan , Johannes Ellemose

2024-10-16T17:00:00Z – 2024-10-16T17:12:00Z GMT-0600 Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: VIS Full Papers

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.

IEEE VIS 2024 Content: EduVis: Workshop on Visualization Education, Literacy, and Activities: EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

EduVis: Workshop on Visualization Education, Literacy, and Activities: EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

https://ieeevis.org/year/2024/program/event_w-eduvis.html

Session chair: Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach

Room: Esplanade Suites I + II + III

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

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Presentations in this session:

Workshop Opening & Outline

Authors:

2024-10-13T12:30:00Z – 2024-10-13T12:40:00Z GMT-0600 Change your timezone on the schedule page

Keynote

Authors:

2024-10-13T12:40:00Z – 2024-10-13T13:10:00Z GMT-0600 Change your timezone on the schedule page

Papers Presentations and Discussion

Authors:

2024-10-13T13:10:00Z – 2024-10-13T13:45:00Z GMT-0600 Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: EduVis: Workshop on Visualization Education, Literacy, and Activities

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.

IEEE VIS 2024 Content: EduVis: Workshop on Visualization Education, Literacy, and Activities: EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

EduVis: Workshop on Visualization Education, Literacy, and Activities: EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities (Session 1)

https://ieeevis.org/year/2024/program/event_w-eduvis.html

Session chair: Fateme Rajabiyazdi, Mandy Keck, Lonni Besancon, Alon Friedman, Benjamin Bach, Jonathan Roberts, Christina Stoiber, Magdalena Boucher, Lily Ge

Room: Esplanade Suites I + II + III

2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-13T12:30:00Z – 2024-10-13T15:30:00Z

Jump to event listing

Presentations in this session:

Workshop Opening & Outline

Authors:

2024-10-13T12:30:00Z – 2024-10-13T12:40:00Z GMT-0600 Change your timezone on the schedule page

Keynote

Authors:

2024-10-13T12:40:00Z – 2024-10-13T13:10:00Z GMT-0600 Change your timezone on the schedule page

Papers Presentations and Discussion

Authors:

2024-10-13T13:10:00Z – 2024-10-13T13:45:00Z GMT-0600 Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: EduVis: Workshop on Visualization Education, Literacy, and Activities

If there are any issues with the virtual streaming site, you can try to access the Discord and Slido pages for this session directly.