Guided Data Discovery in Interactive Visualizations via Active Search
This work addresses the problem of efficient data exploration for analysts dealing with large, complex datasets, representing an incremental advancement by adapting existing active learning methods to visual analytics.
The paper tackles the challenge of guiding users in data exploration within interactive visualizations by applying active learning algorithms, demonstrating performance improvements in a simulation study and validating the approach in a user study for data discovery tasks.
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.