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Provenance for SQL through abstract interpretation: value-less, but worthwhile

Published: 01 August 2015 Publication History

Abstract

We demonstrate the derivation of fine-grained where- and why-provenance for a rich dialect of SQL that includes recursion, (correlated) subqueries, windows, grouping/aggregation, and the RDBMS's library of built-in functions. The approach relies on ideas that originate in the programming language community---program slicing and abstract interpretation, in particular. A two-stage process first records a query's control flow decisions and locations of data access before it derives provenance without consultation of the actual data values (rendering the method largely "value-less"). We will bring an interactive demonstrator that uses this provenance information to make input/output dependencies in real-world SQL queries tangible.

References

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Cited By

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  • (2024)xDBTagger: explainable natural language interface to databases using keyword mappings and schema graphThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00809-w33:2(301-321)Online publication date: 1-Mar-2024
  • (2020)Synthesis of Incremental Linear Algebra ProgramsACM Transactions on Database Systems10.1145/338539845:3(1-44)Online publication date: 26-Aug-2020
  • (2019)Explaining Natural Language query resultsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00584-729:1(485-508)Online publication date: 2-Nov-2019
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  1. Provenance for SQL through abstract interpretation: value-less, but worthwhile

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    Published In

    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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    View all
    • (2024)xDBTagger: explainable natural language interface to databases using keyword mappings and schema graphThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00809-w33:2(301-321)Online publication date: 1-Mar-2024
    • (2020)Synthesis of Incremental Linear Algebra ProgramsACM Transactions on Database Systems10.1145/338539845:3(1-44)Online publication date: 26-Aug-2020
    • (2019)Explaining Natural Language query resultsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00584-729:1(485-508)Online publication date: 2-Nov-2019
    • (2018)How how explains what what computesProceedings of the 10th USENIX Conference on Theory and Practice of Provenance10.5555/3319379.3319391(8-8)Online publication date: 11-Jul-2018
    • (2018)You say 'what', i hear 'where' and 'why'Proceedings of the VLDB Endowment10.14778/3236187.323620411:11(1536-1549)Online publication date: 1-Jul-2018
    • (2017)Provenance for natural language queriesProceedings of the VLDB Endowment10.14778/3055540.305555010:5(577-588)Online publication date: 1-Jan-2017
    • (2017)A survey on provenanceThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-017-0486-126:6(881-906)Online publication date: 1-Dec-2017

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