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Selective provenance for datalog programs using top-k queries

Published: 01 August 2015 Publication History

Abstract

Highly expressive declarative languages, such as datalog, are now commonly used to model the operational logic of data-intensive applications. The typical complexity of such datalog programs, and the large volume of data that they process, call for result explanation. Results may be explained through the tracking and presentation of data provenance, and here we focus on a detailed form of provenance (how-provenance), defining it as the set of derivation trees of a given fact. While informative, the size of such full provenance information is typically too large and complex (even when compactly represented) to allow displaying it to the user. To this end, we propose a novel top-k query language for querying datalog provenance, supporting selection criteria based on tree patterns and ranking based on the rules and database facts used in derivation. We propose an efficient novel algorithm based on (1) instrumenting the datalog program so that, upon evaluation, it generates only relevant provenance, and (2) efficient top-k (relevant) provenance generation, combined with bottom-up datalog evaluation. The algorithm computes in polynomial data complexity a compact representation of the top-k trees which may be explicitly constructed in linear time with respect to their size. We further experimentally study the algorithm performance, showing its scalability even for complex datalog programs where full provenance tracking is infeasible.

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    cover image Proceedings of the VLDB Endowment
    Proceedings of the VLDB Endowment  Volume 8, Issue 12
    Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii
    August 2015
    728 pages
    ISSN:2150-8097
    Issue’s Table of Contents

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    VLDB Endowment

    Publication History

    Published: 01 August 2015
    Published in PVLDB Volume 8, Issue 12

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    • (2023)Online maintenance of evolving knowledge graphs with RDFS-based saturation and why-provenance supportWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2023.10079678:COnline publication date: 1-Oct-2023
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