Learning and Anticipating Future Actions During Exploratory Data Analysis

TL;DR

This paper presents a framework for systems to learn and predict user focus during data analysis by passively observing interactions, significantly improving anticipatory capabilities in visual analytics.

Contribution

It introduces a novel passive observation approach for predicting user actions, enhancing human-computer collaboration in exploratory data analysis.

Findings

Prediction accuracy of 95-97% for user focus areas.

High prediction accuracy achieved after three user interactions.

Passive observation can effectively reveal future user actions.

Abstract

The goal of visual analytics is to create a symbiosis between human and computer by leveraging their unique strengths. While this model has demonstrated immense success, we are yet to realize the full potential of such a human-computer partnership. In a perfect collaborative mixed-initiative system, the computer must possess skills for learning and anticipating the users' needs. Addressing this gap, we propose a framework for inferring focus areas from passive observations of the user's actions, thereby allowing accurate predictions of future events. We evaluate this technique with a crime map and demonstrate that users' clicks appear in our prediction set 95% - 97% of the time. Further analysis shows that we can achieve high prediction accuracy typically after three clicks. Altogether, we show that passive observations of interaction data can reveal valuable information that will allow the system to learn and anticipate future events, laying the foundation for next-generation tools.