User Learning In Interactive Data Exploration

Users explore large, complex datasets to find interesting hypotheses and previously unseen insights. In this process, known as data exploration, users often generate database queries without any precise goals or concrete information need, posing challenges for database systems that assume the user has a clear intent a priori. In response, system developers often model users' exploration strategies over time, which could enable the system to predict and adapt to users' subsequent actions. However, current models generally treat users' exploration behavior as static, whereas in reality, users dynamically change their behavior in response to what they learn during exploration. In this paper, we present an analysis of existing data exploration logs to quantify shifts in users' data exploration strategies over time. Our analysis confirms that users shift their behavior over time, and state-of-the-art learning algorithms struggle to adapt to this evolution, revealing new avenues for building more accurate models of user exploration behavior within data exploration systems.