LensXPlain: Visualizing and Explaining Contributing Subsets for Aggregate Query Answers.

In this demonstration, we will present LensXPlain, an interactive system to help users understand answers of aggregate queries by providing meaningful explanations. Given a SQL group-by query and a question from a user "why output o is high/low", or "why output o1 is higher/lower than o2", LensXPlain helps users explore the results and find subsets of tuples captured by predicates that contributed the most toward such observations. The contributions are measured either by intervention (if the contributing tuples are removed, the values or the ratios in the user question change in the opposite direction), or by aggravation (if the query is restricted to the contributing tuples, the observations change more in the same direction). LensXPlain uses ensemble learning for recommending useful attributes in explanations, and employs a suite of optimizations to enable explanation generation and refinement at an interactive speed. In the demonstration, the audience can run aggregation queries over real world datasets, browse the answers using a graphical user interface, ask questions on unexpected/interesting query results with simple visualizations, and explore and refine explanations returned by LensXPlain.