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Explaining why an answer is (or is not) returned by a query is important formany applications including auditing, debugging data and queries, and answeringhypothetical questions about data. In this work, we present the first practicalapproach for answering such questions for queries with negation (first- orderqueries). Specifically, we introduce a graph-based provenance model that, whilesyntactic in nature, supports reverse reasoning and is proven to encode a widerange of provenance models from the literature. The implementation of thismodel in our PUG (Provenance Unification through Graphs) system takes aprovenance question and Datalog query as an input and generates a Datalogprogram that computes an explanation, i.e., the part of the provenance that isrelevant to answer the question. Furthermore, we demonstrate how a desirablefactorization of provenance can be achieved by rewriting an input query. Weexperimentally evaluate our approach demonstrating its efficiency.

PUG: A Framework and Practical Implementation for Why & Why-Not Provenance (extended version)