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PUG: a framework and practical implementation for why and why-not provenance

  • Seokki Lee
  • Bertram Ludäscher
  • Boris Glavic
Regular Paper
  • 51 Downloads

Abstract

Explaining why an answer is (or is not) returned by a query is important for many applications including auditing, debugging data and queries, and answering hypothetical questions about data. In this work, we present the first practical approach for answering such questions for queries with negation (first-order queries). Specifically, we introduce a graph-based provenance model that, while syntactic in nature, supports reverse reasoning and is proven to encode a wide range of provenance models from the literature. The implementation of this model in our PUG (Provenance Unification through Graphs) system takes a provenance question and Datalog query as an input and generates a Datalog program that computes an explanation, i.e., the part of the provenance that is relevant to answer the question. Furthermore, we demonstrate how a desirable factorization of provenance can be achieved by rewriting an input query. We experimentally evaluate our approach demonstrating its efficiency.

Keywords

Datalog Provenance Missing answers Semirings 

Notes

Acknowledgements

This work was supported by NSF Awards OAC-{1640864, 1541450} and SMA-1637155. Opinions and findings expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Illinois Institute of TechnologyChicagoUSA
  2. 2.University of Illinois, Urbana-Champaign (UIUC)ChampaignUSA

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