WinkComposer is an upcoming Open Source, high-performance JavaScript framework built on Node.js for real-time streaming analytics. It transforms continuous data streams into actionable insights using a reactive processing graph that intelligently manages workloads.
This graph prioritizes control and data messages, manages backpressure effectively, and uses adaptive yielding to stay responsive under varying loads. It ensures that both asynchronous and synchronous operations are executed efficiently without overwhelming the system.
Each graph node is lightweight and modular, designed to execute streaming algorithms in real time. Nodes support both CPU-bound operations—such as filtering, transformation, and specialized algorithms for aggregation, statistical analysis, anomaly detection, and more—and I/O-bound tasks like database queries or LLM-based inference. This versatility makes WinkComposer suitable for domains such as IoT, telematics, smart infrastructure, finance, and healthcare.
WinkComposer goes beyond basic analytics by integrating statistical methods and machine learning algorithms directly into its streaming engine. A built-in knowledge graph unifies business entities, patterns, and events. By persisting it in graph databases like Neo4j or ArangoDB, it forms a digital twin of your domain—enabling semantically rich queries and deeper contextual insights.
At its core, WinkComposer includes an ontology-driven, LLM-powered reasoning layer that analyzes event streams to uncover patterns, identify root causes, and generate real-time, actionable recommendations. This empowers teams to make faster decisions, resolve issues efficiently, and onboard new members more effectively through embedded contextual knowledge.
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Adaptive Yield Threshold keeps the event loop responsive.
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Backpressure Management handles peak loads without memory overflow.
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Priority Scheduling ensures control > data > async messages.
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Built-in Monitoring & Memory Tracking for observability.
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Node-Level Error Isolation prevents local failures from crashing the graph.
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Periodic Snapshots enable quick recovery from faults.
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Define pipelines in a declarative manner.
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Drag-and-drop UI is already planned.
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Simple plugin pattern for custom node development.
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Includes rich starter pack: statistical summaries, anomaly detectors, Count-Min Sketch, MQTT/Kafka sources, and Neo4j/Parquet sinks.
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Emphasis on numerical stability.
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Ontologies modeled in graph DBs like Neo4j or ArangoDB.
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Patterns, aggregates, and events as first-class graph elements.
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Query the digital twin for root cause, impact, and what-if analysis.
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LLM-assisted reasoning explains anomalies and suggests next steps.
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Ease of Use – A declarative design simplifies development and enables rapid iteration.
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Extensibility – Modular, loosely coupled components for easy customization and contributions.
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Open Source & Lightweight LLMs – Prioritizing efficient, accessible models for developers.
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Safety & Security – Aligned with responsible AI principles and OpenSSF best practices.
WinkComposer is in active early development — we invite you to join the conversation on GitHub Discussions or reach out via email at [email protected].
Let’s shape the future of real-time streaming analytics together!
WinkJS is a family of open source packages for Natural Language Processing, Machine Learning, and Statistical Analysis in Javascript. The code is thoroughly documented for easy human comprehension and has a test coverage of ~100% for reliability to build production grade solutions.
WinkComposer is copyright 2024-25 GRAYPE Systems Private Limited.
It is licensed under the terms of the MIT License.