PUG: a framework and practical implementation for why and why-not provenance
Authors
Seokki Lee, Bertram Ludäscher, Boris Glavic
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.
Links
Reference
PUG: a framework and practical implementation for why and why-not provenance Seokki Lee, Bertram Ludäscher, Boris GlavicIn The VLDB Journal, 2018
Bibtex Entry
@article{LL18,
	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.},
	Author = {Lee, Seokki and Lud{\"a}scher, Bertram and Glavic, Boris},
	Keywords = {Datalog; Provenance; Missing Answers; Semirings; PUGS},
    Date-Added = {2018-08-29 19:09:06 -0500},
	Date-Modified = {2018-08-29 19:09:33 -0500},
	Day = {23},
	Doi = {10.1007/s00778-018-0518-5},
	Issn = {0949-877X},
	Journal = {The VLDB Journal},
	Month = {Aug},
	Title = {PUG: a framework and practical implementation for why and why-not provenance},
	Url = {https://doi.org/10.1007/s00778-018-0518-5},
    Longversionurl = {https://arxiv.org/pdf/1808.05752.pdf},
	Year = {2018}}
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