Putting Things into Context: Rich Explanations for Query Answers using Join Graphs

Chenjie Li, Zhengjie Miao, Qitian Zeng, Boris Glavic, Sudeepa Roy

Abstract: In many data analysis applications there is a need to explain why a surprising or interesting result was produced by a query. Previous approaches to explaining results have directly or indirectly relied on data provenance, i.e., input tuples contributing to the result(s) of interest. However, some information that is relevant for explaining an answer may not be contained in the provenance. We propose a new approach for explaining query results by augmenting provenance with information from other related tables in the database. Using a suite of optimization techniques, we demonstrate experimentally using real datasets and through a user study that our approach produces meaningful results and is efficient.