Provenance summaries for answers and non-answers

Seokki Lee, Bertram Ludäscher, Boris Glavic
2018 Proceedings of the VLDB Endowment  
Explaining why an answer is (not) in the result of a query has proven to be of immense importance for many applications. However, why-not provenance, and to a lesser degree also why-provenance, can be very large, even for small input datasets. The resulting scalability and usability issues have limited the applicability of provenance. We present PUG, a system for why and why-not provenance that applies a range of novel techniques to overcome these challenges. Specifically, PUG limits provenance
more » ... capture to what is relevant to explain a (missing) result of interest and uses an efficient sampling-based summarization method to produce compact explanations for (missing) answers. Using two real-world datasets, we demonstrate how a user can draw meaningful insights from explanations produced by PUG.
doi:10.14778/3229863.3236233 fatcat:fxy3gujinbfc7jeun4gvafp7j4