Guided Data Discovery in Interactive Visualizations via Active Search

2020/10/16 by Shayan Monadjemi, Sunwoo Ha, Monadjemi, Shayan +9 · 1 citation
Computer Science · #Data Stream Mining Techniques #Data Visualization and Analytics #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2010.08155

openalex publication_date 2020/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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