Article

Why and Where: A Characterization of Data Provenance

Authors:

Sanjeev Khanna,

Wang Chiew TanAuthors Info & Claims

ICDT '01: Proceedings of the 8th International Conference on Database Theory

Pages 316 - 330

Published: 04 January 2001 Publication History

Abstract

With the proliferation of database views and curated databases, the issue of data provenance - where a piece of data came from and the process by which it arrived in the database - is becoming increasingly important, especially in scientific databases where understanding provenance is crucial to the accuracy and currency of data. In this paper we describe an approach to computing provenance when the data of interest has been created by a database query. We adopt a syntactic approach and present results for a general data model that applies to relational databases as well as to hierarchical data such as XML. A novel aspect of our work is a distinction between "why" provenance (refers to the source data that had some influence on the existence of the data) and "where" provenance (refers to the location(s) in the source databases from which the data was extracted).

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Meng HMiao ZGilad ARoy SYang JDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Characterizing and Verifying Queries Via CINSGENCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589721(143-146)Online publication date: 4-Jun-2023
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Index Terms

Why and Where: A Characterization of Data Provenance
1. Information systems
  1. Data management systems
    1. Database design and models
    2. Database management system engines

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

ICDT '01: Proceedings of the 8th International Conference on Database Theory

January 2001

449 pages

ISBN:3540414568

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 04 January 2001

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Li JMoskovitch YStoyanovich JJagadish H(2023)Query Refinement for Diversity Constraint SatisfactionProceedings of the VLDB Endowment10.14778/3626292.362629517:2(106-118)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.14778/3626292.3626295
Haeberlen APhan LMcGuire M(2023)Metaverse as a ServiceProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624662(298-307)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3620678.3624662
Meng HMiao ZGilad ARoy SYang JDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Characterizing and Verifying Queries Via CINSGENCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589721(143-146)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589721
Amarilli AAmsterdamer YChapman ADeutch DMalik T(2022)Worst-case analysis for interactive evaluation of Boolean provenanceProceedings of the 14th International Workshop on the Theory and Practice of Provenance10.1145/3530800.3534538(1-8)Online publication date: 17-Jun-2022
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Rawat SLee SJung TChapman ADeutch DMalik T(2022)Measuring information gain using provenanceProceedings of the 14th International Workshop on the Theory and Practice of Provenance10.1145/3530800.3534534(1-4)Online publication date: 17-Jun-2022
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Gaur GDang ABhattacharya ABedathur SDemartini GZuccon GCulpepper JHuang ZTong H(2021)Computing and Maintaining Provenance of Query Result Probabilities in Uncertain Knowledge GraphsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482330(545-554)Online publication date: 26-Oct-2021
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