Efficient Answering of Historical What-if Queries

Abstract: We introduce historical what-if queries, a novel type of what-if analysis that determines the effect of a hypothetical change to the transactional history of a database. For example, "how would revenue be affected if we would have charged an additional $6 for shipping?" Such queries may lead to more actionable insights than traditional what-if queries as their results can be used to inform future actions, e.g., increasing shipping fees. We develop efficient techniques for answering historical what-if queries, i.e., determining how a modified history affects the current database state. Our techniques are based on reenactment, a replay technique for transactional histories. We optimize this process using program and data slicing techniques that determine which updates and what data can be excluded from reenactment without affecting the result. Using an implementation of our techniques in Mahif (a Middleware for Answering Historical what-IF queries) we demonstrate their effectiveness experimentally.