We present the first query-based approach for explaining missing answers to queries over nested relational data which is a common data format used by big data systems such as Apache Spark. Our main contributions are a novel way to define query-based why-not provenance based on repairs to queries and presenting an implementation and preliminary experiments for answering such queries in Spark.
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PUG: a framework and practical implementation for why and why-not provenance
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.
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- Award ID(s):
- 1640864
- NSF-PAR ID:
- 10082094
- Date Published:
- Journal Name:
- The VLDB Journal
- ISSN:
- 1066-8888
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s00778-018-0518-5
