Predicting “What is interesting” by mining interactive-data-analysis session logs

Assessing the interestingness of data analysis actions has been the subject of extensive previous work, and a multitude of interestingness measures have been devised, each capturing a different facet of the broad concept. While such measures are a core component in many analysis platforms (e.g., for ranking association rules, recommending visualizations, and query formulation), choosing the most adequate measure for a specific analysis task or an application domain is known to be a difficult task. In this work we focus on the choice of interestingness measures particularly for Interactive Data Analysis (IDA), where users examine datasets by performing sessions of analysis actions. Our goal is to determine the most suitable interestingness measure that adequately captures the user’s current interest at each step of an interactive analysis session. We propose a novel solution that is based on the mining of IDA session logs. First, we perform an offline analysis of the logs, and identify unique characteristics of interestingness in IDA sessions. We then define a classification problem and build a predictive model that can select the best measure for a given a state of a user session. Our experimental evaluation, performed over real-life session logs, demonstrates the sensibility and adequacy of our approach.