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Efficient provenance tracking for datalog using top-k queries

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, defined here 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 that 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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Notes

  1. This requires a slight change of the definition of patterns, which is easy to support, to allow * in relation names.

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Acknowledgements

This research has been partially funded by the Israeli Science Foundation (978/17, 1636/13) and the Blavatnik Interdisciplinary Cyber Research Center (TAU ICRC). The contribution of Yuval Moskovitch is part of Ph.D. thesis research conducted at Tel Aviv University.

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Correspondence to Amir Gilad.

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Deutch, D., Gilad, A. & Moskovitch, Y. Efficient provenance tracking for datalog using top-k queries. The VLDB Journal 27, 245–269 (2018). https://doi.org/10.1007/s00778-018-0496-7

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  • DOI: https://doi.org/10.1007/s00778-018-0496-7

Keywords

  • Provenance
  • Datalog
  • Top-K