Article

Free access

Consistent query answers in inconsistent databases

Authors:

Marcelo Arenas,

Leopoldo Bertossi,

Jan ChomickiAuthors Info & Claims

PODS '99: Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

Pages 68 - 79

https://doi.org/10.1145/303976.303983

Published: 01 May 1999 Publication History

PDF eReader

References

[1]

S. Agarwal, A.M. Keller, G. Wiederhold, and K. Saraswat. Flexible Relation: An Approach for Integrating Data from Multiple, Possibly Inconsistent Databases. In IEEE International Conference on Data Engineering, 1995.

Digital Library

Google Scholar

[2]

M. Arenas, L. Bertossi, and M. Kifer. APC and Querying Inconsistent Databases. In preparation.

Google Scholar

[3]

C. Baral, S. Kraus, J. Minker, and V.S. Subrahmanian. Combining Knowledge Bases Consisting of First- Order Theories. Computational Intelligence, 8:45-71, 1992.

Google Scholar

[4]

E Bry. Query Answering in Information Systems with Integrity Constraints. In IFIP WG 11.5 Working Conference on Integrity and Control in Information Systems. Chapman &Hall, 1997.

Digital Library

Google Scholar

[5]

U.S. Chakravarthy, J. Grant, and J. Minker. Logic- Based Approach to Semantic Query Optimization. ACM Transactions on Database Systems, 15(2):162- 207, 1990.

Digital Library

Google Scholar

[6]

S. Chaudhuri and U. Dayal. An Overview of Data Warehousing and OLAP Technology. SIGMOD Record, 26, March 1997.

Digital Library

Google Scholar

[7]

J. Chomicki and G. Saake, editors. Logics for Databases and Information Systems. Kluwer Academic Publishers, Boston, 1998.

Digital Library

Google Scholar

[8]

T. Chou and M. Winslett. A Model-Based Belief Revision System. J. Automated Reasoning, 12:157-208, 1994.

Crossref

Google Scholar

[9]

Phan Minh Dung. Integrating Data from Possibly Inconsistent Databases. In International Conference on Cooperative Information Systems, Brussels, Belgium, 1996.

Digital Library

Google Scholar

[10]

P. Gaerdenfors and H. Rott. Belief Revision. In D. M. Gabbay, J. Hogger, C, and J. A. Robinson, editors, Handbook of Logic in Artificial Intelligence and Logic Programming, volume 4, pages 35-132. Oxford University Press, 1995.

Digital Library

Google Scholar

[11]

M. Gertz. Diagnosis and Repair of Constraint Violations in Database Systems. PhD thesis, Universitfit Hannover, 1996.

Google Scholar

[12]

P. Godfrey, J. Grant, J. Gryz, and J. Minker. Integrity Constraints: Semantics and Applications. In Chomicki and Saake {7}, chapter 9.

Digital Library

Google Scholar

[13]

H. Katsuno and A. Mendelzon. Propositional Knowledge Base Revision and Minimal Change. Artificial Intelligence, 52:263-294, 1991.

Digital Library

Google Scholar

[14]

K.L. Kwast. A Deontic Approach to Database Integrity. Annals of Mathematics and Artificial Intelligence, 9:205-238, 1993.

Crossref

Google Scholar

[15]

J. Lin. A Semantics for Reasoning Consistently in the Presence of Inconsistency. Artificial Intelligence, 86(1- 2):75-95, 1996.

Digital Library

Google Scholar

[16]

J. Lin and A. O. Mendelzon. Merging Databases under Constraints. International Journal of C~ooperative Information Systems, 7(1):55-76, 1996.

Crossref

Google Scholar

[17]

'W.W. McCune. OTTER 3.0 Reference Manual and Guide. Argonne National Laboratory, Technical Report ANL-94/6, 1994.

Google Scholar

[18]

J.-J. Meyer, R. Wieringa, and E Dignum. The Role of Deontic Logic in the Specification of Information Systems. In Chomicki and Saake {7}, chapter 4.

Digital Library

Google Scholar

[19]

Jean-Marie Nicolas. Logic for Improving Integrity Checking in Relational Data Bases. Acta Informatica, 18:227-253, lgP82.

Google Scholar

[20]

J. Ullman. Principles of Database and Knowledge- Base Systems, Vol. I. Computer Science Press, 1988.

Digital Library

Google Scholar

Cited By

View all

Bao ZBie BFan WLi DLi MLin KLin WLiu PLiu PLv ZOuyang MSun CTang SWang YWei QWu XXie MZhang JZhao RZhu JZhu Y(2024)Rock: Cleaning Data with both ML and Logic RulesProceedings of the VLDB Endowment10.14778/3685800.368587817:12(4373-4376)Online publication date: 8-Nov-2024
https://doi.org/10.14778/3685800.3685878
Yuan YTang BZhou TZhang ZQin J(2024)nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic SystemsProceedings of the VLDB Endowment10.14778/3681954.368200017:11(3283-3289)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3682000
Yan MFan WWang YXie M(2024)Enriching Relations with Additional Attributes for ERProceedings of the VLDB Endowment10.14778/3681954.368198717:11(3109-3123)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681987
Show More Cited By

Index Terms

Consistent query answers in inconsistent databases

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 answers in inconsistent probabilistic databases
SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Efficient and effective manipulation of probabilistic data has become increasingly important recently due to many real applications that involve the data uncertainty. This is especially crucial when probabilistic data collected from different sources ...
Probabilistic query answering over inconsistent databases

This paper presents a framework for querying inconsistent databases in the presence of functional dependencies. Most of the works dealing with the problem of extracting reliable information from inconsistent databases are based on the notion of repair, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

PODS '99: Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

May 1999

374 pages

ISBN:1581130627

DOI:10.1145/303976

Chairmen:
Victor Vianu
Univ. of California, San Diego
,
Christos Papadimitriou
Univ. of California, Berkeley

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 1999

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

SIGMOD/PODS99

Sponsor:

SIGMOD/PODS99: International Conference on Management of Data and Symposium on Principles of Database Systems

May 31 - June 3, 1999

Pennsylvania, Philadelphia, USA

Acceptance Rates

PODS '99 Paper Acceptance Rate 32 of 116 submissions, 28%;

Overall Acceptance Rate 642 of 2,707 submissions, 24%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

557
Total Citations
View Citations
1,586
Total Downloads

Downloads (Last 12 months)207
Downloads (Last 6 weeks)31

Reflects downloads up to 27 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Bao ZBie BFan WLi DLi MLin KLin WLiu PLiu PLv ZOuyang MSun CTang SWang YWei QWu XXie MZhang JZhao RZhu JZhu Y(2024)Rock: Cleaning Data with both ML and Logic RulesProceedings of the VLDB Endowment10.14778/3685800.368587817:12(4373-4376)Online publication date: 8-Nov-2024
https://doi.org/10.14778/3685800.3685878
Yuan YTang BZhou TZhang ZQin J(2024)nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic SystemsProceedings of the VLDB Endowment10.14778/3681954.368200017:11(3283-3289)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3682000
Yan MFan WWang YXie M(2024)Enriching Relations with Additional Attributes for ERProceedings of the VLDB Endowment10.14778/3681954.368198717:11(3109-3123)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681987
Fan WPang KLu PTian C(2024)Making It Tractable to Detect and Correct Errors in GraphsACM Transactions on Database Systems10.1145/3702315Online publication date: 2-Nov-2024
https://doi.org/10.1145/3702315
Fan WHan ZXie MZhang G(2024)Discovering Top-k Relevant and Diversified RulesProceedings of the ACM on Management of Data10.1145/36771312:4(1-28)Online publication date: 30-Sep-2024
https://doi.org/10.1145/3677131
Mecca GPapotti PSantoro DVeltri E(2024)BUNNI: Learning Repair Actions in Rule-driven Data CleaningJournal of Data and Information Quality10.1145/366593016:2(1-31)Online publication date: 25-May-2024
https://dl.acm.org/doi/10.1145/3665930
Calautti MLivshits EPieris ASchneider M(2024)Combined Approximations for Uniform Operational Consistent Query AnsweringProceedings of the ACM on Management of Data10.1145/36516002:2(1-16)Online publication date: 14-May-2024
https://doi.org/10.1145/3651600
Padmanabha ASegoufin LSirangelo C(2024)A Dichotomy in the Complexity of Consistent Query Answering for Two Atom Queries With Self-JoinProceedings of the ACM on Management of Data10.1145/36511372:2(1-15)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3651137
Kimelfeld BKolaitis P(2024)A Unifying Framework for Incompleteness, Inconsistency, and Uncertainty in DatabasesCommunications of the ACM10.1145/362471767:3(74-83)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3624717
Middelburg C(2024)Paraconsistent logic and query answering in inconsistent databasesJournal of Applied Non-Classical Logics10.1080/11663081.2024.231277634:1(133-154)Online publication date: 8-Feb-2024
https://doi.org/10.1080/11663081.2024.2312776
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

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

Cited By

Index Terms

Recommendations

First-order under-approximations of consistent query answers

Consistent query answers in inconsistent probabilistic databases

Probabilistic query answering over inconsistent databases