  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 used data provenance (input
tuples contributing to the result(s) of interest), which is limited by the fact
that relevant information for explaining an answer may not be fully 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. We develop a suite of optimization techniques, and demonstrate
experimentally using real datasets and through a user study that our approach
produces meaningful results by efficiently navigating the large search space of
possible explanations.
