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On approximating optimum repairs for functional dependency violations

Authors:

Laks V. S. LakshmananAuthors Info & Claims

ICDT '09: Proceedings of the 12th International Conference on Database Theory

Pages 53 - 62

https://doi.org/10.1145/1514894.1514901

Published: 23 March 2009 Publication History

Abstract

We study the problem of repairing an inconsistent database that violates a set of functional dependencies by making the smallest possible value modifications. For an inconsistent database, we define an optimum repair as a database that satisfies the functional dependencies, and minimizes, among all repairs, a distance measure that depends on the number of corrections made in the database and the weights of tuples modified. We show that like other versions of the repair problem, checking the existence of a repair within a certain distance of a database is NP-complete. We also show that finding a constant-factor approximation for the optimum repair for any set of functional dependencies is NP-hard. Furthermore, there is a small constant and a set of functional dependencies, for which finding an approximate solution for the optimum repair within the factor of that constant is also NP-hard. Then we present an approximation algorithm that for a fixed set of functional dependencies and an arbitrary input inconsistent database, produces a repair whose distance to the database is within a constant factor of the optimum repair distance. We finally show how the approximation algorithm can be used in data cleaning using a recent extension to functional dependencies, called conditional functional dependencies.

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Cited By

Ke JZacouris ZAcosta M(2024)Efficient Validation of SHACL Shapes with ReasoningProceedings of the VLDB Endowment10.14778/3681954.368202317:11(3589-3601)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682023
Pirhadi AMoslemi MCloninger AMilani MSalimi B(2024)OTClean: Data Cleaning for Conditional Independence Violations using Optimal TransportProceedings of the ACM on Management of Data10.1145/36549632:3(1-26)Online publication date: 30-May-2024
https://doi.org/10.1145/3654963
Carmeli NGrohe MKimelfeld BLivshits ETibi M(2024)Database Repairing with Soft Functional DependenciesACM Transactions on Database Systems10.1145/365115649:2(1-34)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3651156
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Index Terms

On approximating optimum repairs for functional dependency violations
1. Information systems
  1. Data management systems
    1. Database management system engines

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cover image ACM Other conferences

ICDT '09: Proceedings of the 12th International Conference on Database Theory

March 2009

334 pages

ISBN:9781605584232

DOI:10.1145/1514894

Editor:
Ronald Fagin
IBM Research

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 March 2009

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EDBT/ICDT '09

EDBT/ICDT '09: EDBT/ICDT '09 joint conference

March 23 - 25, 2009

St. Petersburg, Russia

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Cited By

Ke JZacouris ZAcosta M(2024)Efficient Validation of SHACL Shapes with ReasoningProceedings of the VLDB Endowment10.14778/3681954.368202317:11(3589-3601)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682023
Pirhadi AMoslemi MCloninger AMilani MSalimi B(2024)OTClean: Data Cleaning for Conditional Independence Violations using Optimal TransportProceedings of the ACM on Management of Data10.1145/36549632:3(1-26)Online publication date: 30-May-2024
https://doi.org/10.1145/3654963
Carmeli NGrohe MKimelfeld BLivshits ETibi M(2024)Database Repairing with Soft Functional DependenciesACM Transactions on Database Systems10.1145/365115649:2(1-34)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3651156
Qin JHuang SWang YZhu JZhang YMiao YMao ROnizuka MXiao C(2024)BClean: A Bayesian Data Cleaning System2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00263(3407-3420)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00263
Yang QTang BDeng LMing Z(2024)A hybrid physics-corrected neural network for RUL prognosis under random missing dataExpert Systems with Applications10.1016/j.eswa.2024.124939(124939)Online publication date: Aug-2024
https://doi.org/10.1016/j.eswa.2024.124939
Wang HZhang AZhang JGuo NXia X(2024)Computing Minimum Subset Repair on Incomplete DataWeb and Big Data10.1007/978-981-97-7238-4_28(444-459)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7238-4_28
Zhang AHu SZong CLi JXia X(2024)Computing Maximal Likelihood Subset Repair for Inconsistent DataWeb and Big Data10.1007/978-981-97-2390-4_1(1-15)Online publication date: 28-Apr-2024
https://doi.org/10.1007/978-981-97-2390-4_1
Su YGong YSong S(2023)Time Series Data ValidityProceedings of the ACM on Management of Data10.1145/35889391:1(1-26)Online publication date: 30-May-2023
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Sun YSong SYuan X(2023)From Minimum Change to Maximum Density: On Determining Near-Optimal S-RepairIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3294401(1-12)Online publication date: 2023
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Wang RLi YWang J(2023)Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00391(1502-1515)Online publication date: Apr-2023
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