Guided Data Discovery in Interactive Visualizations via Active Search

TL;DR

This paper explores how active learning algorithms can be used to guide users in interactive data visualizations, especially for large and complex datasets, by matching algorithms to tasks and validating with user studies.

Contribution

It introduces a tailored active learning algorithm for guiding data discovery in visual analytics and demonstrates its effectiveness through simulation and user studies.

Findings

Matching active learning algorithms to tasks improves performance.

The proposed algorithm effectively guides users in data discovery.

User study confirms the utility of active guidance in visual analytics.

Abstract

Recent advances in visual analytics have enabled us to learn from user interactions and uncover analytic goals. These innovations set the foundation for actively guiding users during data exploration. Providing such guidance will become more critical as datasets grow in size and complexity, precluding exhaustive investigation. Meanwhile, the machine learning community also struggles with datasets growing in size and complexity, precluding exhaustive labeling. Active learning is a broad family of algorithms developed for actively guiding models during training. We will consider the intersection of these analogous research thrusts. First, we discuss the nuances of matching the choice of an active learning algorithm to the task at hand. This is critical for performance, a fact we demonstrate in a simulation study. We then present results of a user study for the particular task of data discovery guided by an active learning algorithm specifically designed for this task.