-ds "dataset_behavioral_dimension" \
-rd "2019-07-15" \
-d "," \
-f "/Users/brian/Documents/synthetic3.csv" \
-bd "labcode"
{% hint style="info" %} Track the counts for distinct columns or relationships for behavioral analytics on individual column components {% endhint %}
-bd "labcode"
Go beyond just tracking distinct values per the entire column.
For example, if you had a column (or multiple columns) that typically contained 5 values and each of those values typically represented 20% of the column.
ColumnA
a -> 20
b -> 20
c -> 20
d -> 20
e -> 20
Consider the row count remained 100 and the total distinct values remained 5, but suddenly the proportion of a given value changed drastically.
ColumnA
a -> 1
b -> 39
c -> 20
d -> 20
e -> 20
Behavioral dimension would flag that ColumnA value a is represented much less than normal and value b is represented much more than normal.