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Conference Paper: Evaluating probabilistic queries over uncertain matching

TitleEvaluating probabilistic queries over uncertain matching
Authors
KeywordsDatabase schemas
Fast algorithms
Machine learning techniques
Query evaluation
Query performance
Issue Date2012
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178
Citation
The IEEE 28th International Conference on Data Engineering (ICDE 2012), Washington, D.C., 1-5 April 2012. In International Conference on Data Engineering Proceedings, 2012, p. 1096-1107 How to Cite?
AbstractA matching between two database schemas, generated by machine learning techniques (e.g., COMA++), is often uncertain. Handling the uncertainty of schema matching has recently raised a lot of research interest, because the quality of applications rely on the matching result. We study query evaluation over an inexact schema matching, which is represented as a set of 'possible mappings', as well as the probabilities that they are correct. Since the number of possible mappings can be large, evaluating queries through these mappings can be expensive. By observing the fact that the possible mappings between two schemas often exhibit a high degree of overlap, we develop two efficient solutions. We also present a fast algorithm to compute answers with the k highest probabilities. An extensive evaluation on real schemas shows that our approaches improve the query performance by almost an order of magnitude. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/164909
ISSN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCheng, Ren_US
dc.contributor.authorGong, Jen_US
dc.contributor.authorCheung, DWLen_US
dc.contributor.authorCheng, Jen_US
dc.date.accessioned2012-09-20T08:12:20Z-
dc.date.available2012-09-20T08:12:20Z-
dc.date.issued2012en_US
dc.identifier.citationThe IEEE 28th International Conference on Data Engineering (ICDE 2012), Washington, D.C., 1-5 April 2012. In International Conference on Data Engineering Proceedings, 2012, p. 1096-1107en_US
dc.identifier.issn1084-4627-
dc.identifier.urihttp://hdl.handle.net/10722/164909-
dc.description.abstractA matching between two database schemas, generated by machine learning techniques (e.g., COMA++), is often uncertain. Handling the uncertainty of schema matching has recently raised a lot of research interest, because the quality of applications rely on the matching result. We study query evaluation over an inexact schema matching, which is represented as a set of 'possible mappings', as well as the probabilities that they are correct. Since the number of possible mappings can be large, evaluating queries through these mappings can be expensive. By observing the fact that the possible mappings between two schemas often exhibit a high degree of overlap, we develop two efficient solutions. We also present a fast algorithm to compute answers with the k highest probabilities. An extensive evaluation on real schemas shows that our approaches improve the query performance by almost an order of magnitude. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178-
dc.relation.ispartofInternational Conference on Data Engineering Proceedingsen_US
dc.rightsInternational Conference on Data Engineering Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectDatabase schemas-
dc.subjectFast algorithms-
dc.subjectMachine learning techniques-
dc.subjectQuery evaluation-
dc.subjectQuery performance-
dc.titleEvaluating probabilistic queries over uncertain matchingen_US
dc.typeConference_Paperen_US
dc.identifier.emailCheng, R: ckcheng@cs.hku.hken_US
dc.identifier.emailGong, J: jgong@cs.hku.hken_US
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hken_US
dc.identifier.emailCheng, J: jf.cheng@siat.ac.cn-
dc.identifier.authorityCheng, R=rp00074en_US
dc.identifier.authorityCheung, DWL=rp00101en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDE.2012.14-
dc.identifier.scopuseid_2-s2.0-84864204535-
dc.identifier.hkuros206213en_US
dc.identifier.spage1096-
dc.identifier.epage1107-
dc.identifier.isiWOS:000309122100096-
dc.publisher.placeUnited States-
dc.description.otherThe IEEE 28th International Conference on Data Engineering (ICDE 2012), Washington, D.C., 1-5 April 2012. In International Conference on Data Engineering Proceedings, 2012, p. 1096-1107-

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