Provenance summaries for answers and nonanswers

Seokki Lee, Bertram Ludäscher, Boris Glavic

Research output: Contribution to journalConference articlepeer-review


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 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.

Original languageEnglish (US)
Pages (from-to)1954-1957
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
StatePublished - 2018
Event44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil
Duration: Aug 27 2018Aug 31 2018

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Computer Science


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