Article

Computing consistent query answers using conflict hypergraphs

Authors:

Jerzy Marcinkowski,

Slawomir StaworkoAuthors Info & Claims

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

Pages 417 - 426

https://doi.org/10.1145/1031171.1031254

Published: 13 November 2004 Publication History

Abstract

A consistent query answer in a possibly inconsistent database is an answer which is true in every (minimal) repair of the database. We present here a practical framework for computing consistent query answers for large, possibly inconsistent relational databases. We consider relational algebra queries without projection, and denial constraints. Because our framework handles union queries, we can effectively (and efficiently) extract indefinite disjunctive information from an inconsistent database. We describe a number of novel optimization techniques applicable in this context and summarize experimental results that validate our approach.

References

[1]

S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison-Wesley, 1995.]]

Digital Library

[2]

M. Arenas, L. Bertossi, and J. Chomicki. Consistent Query Answers in Inconsistent Databases. In ACM Symposium on Principles of Database Systems (PODS), pages 68--79, 1999.]]

Digital Library

[3]

M. Arenas, L. Bertossi, and J. Chomicki. Answer Sets for Consistent Query Answering in Inconsistent Databases. Theory and Practice of Logic Programming, 3(4--5):393--424, 2003.]]

Digital Library

[4]

M. Arenas, L. Bertossi, J. Chomicki, X. He, V. Raghavan, and J. Spinrad. Scalar Aggregation in Inconsistent Databases. Theoretical Computer Science, 296(3):405--434, 2003.]]

Digital Library

[5]

M. Arenas, L. Bertossi, and M. Kifer. Applications of Annotated Predicate Calculus to Querying Inconsistent Databases. In International Conference on Computational Logic, pages 926--941. Springer-Verlag, LNCS 1861, 2000.]]

Digital Library

[6]

P. Barcelo and L. Bertossi. Logic Programs for Querying Inconsistent Databases. In International Symposium on Practical Aspects of Declarative Languages (PADL), pages 208--222. Springer-Verlag, LNCS 2562, 2003.]]

Digital Library

[7]

L. Bertossi and J. Chomicki. Query Answering in Inconsistent Databases. In J. Chomicki, R. van der Meyden, and G. Snake, editors, Logics for Emerging Applications of Databases, pages 43--83. Springer-Verlag, 2003.]]

[8]

L. Bertossi, J. Chomicki, A. Cortes, and C. Gutierrez. Consistent Answers from Integrated Data Sources. In International Conference on Flexible Query Answering Systems (FQAS), pages 71--85, Copenhagen, Denmark, October 2002. Springer-Verlag.]]

Digital Library

[9]

L. Bravo and L. Bertossi. Logic Programs for Consistently Querying Data Integration Systems. In International Joint Conference on Artificial Intelligence (IJCAI), pages 10--15, 2003.]]

Digital Library

[10]

F. Bry. Query Answering in Information Systems with Integrity Constraints. In IFIP WG 11.5 Working Conference on Integrity and Control in Information Systems, pages 113--130. Chapman & Hall, 1997.]]

Digital Library

[11]

A. Cali, D. Lembo, and R. Rosati. On the Decidability and Complexity of Query Answering over Inconsistent and Incomplete Databases. In ACM Symposium on Principles of Database Systems (PODS), pages 260--271, 2003.]]

Digital Library

[12]

A. Celle and L. Bertossi. Querying Inconsistent Databases: Algorithms and Implementation. In International Conference on Computational Logic, pages 942--956. Springer-Verlag, LNCS 1861, 2000.]]

Digital Library

[13]

J. Chomicki and J. Marcinkowski. Minimal-Change Integrity Maintenance Using Tuple Deletions. Information and Computation, 2004. To appear. Earlier version: Technical Report cs.DB/0212004, arXiv.org e-Print archive.]]

Digital Library

[14]

T. Eiter, W. Faber, N. Leone, and G. Pfeifer. Declarative Problem-Solving in DLV. In J. Minker, editor, Logic-Based Artificial Intelligence, pages 79--103. Kluwer, 2000.]]

Digital Library

[15]

T. Eiter, M. Fink, G. Greco, and D. Lembo. Efficient Evaluation of Logic Programs for Querying Data Integration Systems. In International Conference on Logic Programming (ICLP), pages 163--177, 2003.]]

[16]

R. Fagin, P. G. Kolaitis, R. J. Miller, and L. Popa. Data Exchange: Semantics and Query Answering. In International Conference on Database Theory (ICDT), pages 207--224. Springer-Verlag, LNCS 2572, 2003.]]

Digital Library

[17]

A. Fuxman and R. Miller. Towards Inconsistency Management in Data Integration Systems. In IJCAI-03 Workshop on Information Integration on the Web (IIWeb-03), 2003.]]

[18]

G. Greco, S. Greco, and E. Zumpano. A Logic Programming Approach to the Integration, Repairing and Querying of Inconsistent Databases. In International Conference on Logic Programming (ICLP), pages 348--364. Springer-Verlag, LNCS 2237, 2001.]]

Digital Library

[19]

G. Greco, S. Greco, and E. Zumpano. A Logical Framework for Querying and Repairing Inconsistent Databases. IEEE Transactions on Knowledge and Data Engineering, 15(6):1389--1408, 2003.]]

Digital Library

[20]

A. Y. Halevy. Answering Queries Using Views: A Survey. VLDB Journal, 10(4):270--294, 2001.]]

Digital Library

[21]

P. C. Kanellakis. Elements of Relational Database Theory. In Jan van Leeuwen, editor, Handbook of Theoretical Computer Science, volume~B, chapter~17, pages 1073--1158. Elsevier/MIT Press, 1990.]]

Digital Library

[22]

D. Van Nieuwenborgh and D. Vermeir. Preferred Answer Sets for Ordered Logic Programs. In European Conference on Logics for Artificial Intelligence (JELIA), pages 432--443. Springer-Verlag, LNAI 2424, 2002.]]

Digital Library

[23]

J. Wijsen. Condensed Representation of Database Repairs for Consistent Query Answering. In International Conference on Database Theory (ICDT), pages 378--393. Springer-Verlag, LNCS 2572, 2003.]]

Digital Library

Cited By

Parisi FGrant J(2023)On measuring inconsistency in definite and indefinite databases with denial constraintsArtificial Intelligence10.1016/j.artint.2023.103884318(103884)Online publication date: May-2023
https://doi.org/10.1016/j.artint.2023.103884
Dixit AKolaitis P(2022)Consistent Answers of Aggregation Queries via SAT2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00074(924-937)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00074
Dixit AKolaitis PLi GLi ZIdreos SSrivastava D(2021)CAvSAT: Answering Aggregation Queries over Inconsistent Databases via SAT SolvingProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452749(2701-2705)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452749
Show More Cited By

Index Terms

Computing consistent query answers using conflict hypergraphs
1. Information systems
  1. Data management systems
    1. Database management system engines
    2. Query languages
2. Theory of computation

Recommendations

First-order under-approximations of consistent query answers

Consistent Query Answering (CQA) is a principled approach for answering queries on inconsistent databases. The consistent answer to a query q on an inconsistent database db is the intersection of the answers to q on all repairs, where a repair is any ...
Consistent query answering under key and exclusion dependencies: algorithms and experiments
CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Research in consistent query answering studies the definition and computation of "meaningful" answers to queries posed to inconsistent databases, i.e., databases whose data do not satisfy the integrity constraints (ICs) declared on their schema. ...
The consistency extractor system: Answer set programs for consistent query answering in databases

We describe the Consistency Extractor System (Cons Ex) that computes consistent answers to Datalog queries with negation posed to relational databases that may be inconsistent with respect to certain integrity constraints. In order to solve this task, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

November 2004

678 pages

ISBN:1581138741

DOI:10.1145/1031171

General Chair:
David Grossman
Illinois Institute of Technology
,
Program Chairs:
Luis Gravano
Columbia University
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign
,
Otthein Herzog
University of Bremen, Germany
,
David A. Evans
Clairvoyance Corporation

Copyright © 2004 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2004

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

CIKM04

Sponsor:

CIKM04: Conference on Information and Knowledge Management

November 8 - 13, 2004

D.C., Washington, USA

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

71
Total Citations
View Citations
454
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 07 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Parisi FGrant J(2023)On measuring inconsistency in definite and indefinite databases with denial constraintsArtificial Intelligence10.1016/j.artint.2023.103884318(103884)Online publication date: May-2023
https://doi.org/10.1016/j.artint.2023.103884
Dixit AKolaitis P(2022)Consistent Answers of Aggregation Queries via SAT2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00074(924-937)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00074
Dixit AKolaitis PLi GLi ZIdreos SSrivastava D(2021)CAvSAT: Answering Aggregation Queries over Inconsistent Databases via SAT SolvingProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452749(2701-2705)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452749
Perez HWijsen J(2021)Generalized Weighted RepairsFlexible Query Answering Systems10.1007/978-3-030-86967-0_6(67-81)Online publication date: 16-Sep-2021
https://doi.org/10.1007/978-3-030-86967-0_6
Issa OBonifati AToumani F(2020)Evaluating top-k queries with inconsistency degreesProceedings of the VLDB Endowment10.14778/3407790.340781513:12(2146-2158)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.14778/3407790.3407815
Cohen SRosenthal AZohar A(2020)Reasoning about the Future in Blockchain Databases2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00206(1930-1933)Online publication date: Apr-2020
https://doi.org/10.1109/ICDE48307.2020.00206
Bienvenu MBourgaux CGoasdoué F(2019)Computing and explaining query answers over inconsistent DL-lite knowledge basesJournal of Artificial Intelligence Research10.1613/jair.1.1139564:1(563-644)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1613/jair.1.11395
Dixit ABoncz PManegold SAilamaki ADeshpande AKraska T(2019)CAvSATProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300095(1823-1825)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3300095
Dixit AKolaitis P(2019)A SAT-Based System for Consistent Query AnsweringTheory and Applications of Satisfiability Testing – SAT 201910.1007/978-3-030-24258-9_8(117-135)Online publication date: 29-Jun-2019
https://doi.org/10.1007/978-3-030-24258-9_8
Ray JJohnny OTrovati MSotiriadis SBessis N(2018)The Rise of Big Data Science: A Survey of Techniques, Methods and Approaches in the Field of Natural Language Processing and Network TheoryBig Data and Cognitive Computing10.3390/bdcc20300222:3(22)Online publication date: 2-Aug-2018
https://doi.org/10.3390/bdcc2030022
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents