Incremental Information Gain Mining Of Temporal Relational Streams

TL;DR

This paper presents an efficient method for incrementally maintaining and monitoring high information gain in temporal relational data streams, aiding real-time data analysis and insight discovery.

Contribution

It introduces an incremental approach to identify and track high information gain in continuously updated temporal relational tables, enabling real-time insights.

Findings

Efficient incremental maintenance of information gain values

Real-time monitoring of high information gain in data streams

Applicable to continuous data analysis scenarios

Abstract

This paper studies the problem of mining for data values with high information gain in relational tables. High information gain can help data analysts and secondary data mining algorithms gain insights into strong statistical dependencies and causality relationship between key metrics. In this paper, we will study the problem of high information gain identification for scenarios involving temporal relations where new records are added continuously to the relations. We show that information gain can be efficiently maintained in an incremental fashion, making it possible to monitor continuously high information gain values.