CAPE: Explaining Outliers by Counterbalancing.

In this demonstration we showcase Cape, a system that explains surprising aggregation outcomes. In contrast to previous work, which relies exclusively on provenance, Cape explains outliers in aggregation queries through related outliers in the opposite direction that provide counterbalance. The foundation of our approach are aggregate regression patterns (ARPs) that describe coarse-grained trends in the data. We define outliers as deviations from such patterns and present an efficient algorithm to find counterbalances explaining outliers. In the demonstration, the audience can run aggregation queries over real world datasets, identify outliers of interest in the result of such queries, and browse the patterns and explanations returned by Cape.