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Article: Cleaning uncertain data with quality guarantees

TitleCleaning uncertain data with quality guarantees
Authors
Issue Date2008
PublisherVery Large Data Base (VLDB) Endowment Inc.. The Journal's web site is located at http://vldb.org/pvldb/index.html
Citation
Proceedings of the VLDB Endowment, 2008, v. 1, p. 722-735 How to Cite?
AbstractUncertain or imprecise data are pervasive in applications like location-based services, sensor monitoring, and data collection and integration. For these applications, probabilistic databases can be used to store uncertain data, and querying facilities are provided to yield answers with statistical confidence. Given that a limited amount of resources is available to “clean” the database (e.g., by probing some sensor data values to get their latest values), we address the problem of choosing the set of uncertain objects to be cleaned, in order to achieve the best improvement in the quality of query answers. For this purpose, we present the PWS-quality metric, which is a universal measure that quantifies the ambiguity of query answers under the possible world semantics. We study how PWS-quality can be efficiently evaluated for two major query classes: (1) queries that examine the satisfiability of tuples independent of other tuples (e.g., range queries); and (2) queries that require the knowledge of the relative ranking of the tuples (e.g., MAX queries). We then propose a polynomial-time solution to achieve an optimal improvement in PWS-quality. Other fast heuristics are presented as well. Experiments, performed on both real and synthetic datasets, show that the PWS-quality metric can be evaluated quickly, and that our cleaning algorithm provides an optimal solution with high efficiency. To our best knowledge, this is the first work that develops a quality metric for a probabilistic database, and investigates how such a metric can be used for data cleaning purposes.
Persistent Identifierhttp://hdl.handle.net/10722/61148
ISSN

 

DC FieldValueLanguage
dc.contributor.authorCheng, CKen_HK
dc.contributor.authorChen, J-
dc.contributor.authorXie, X-
dc.date.accessioned2010-07-13T03:31:57Z-
dc.date.available2010-07-13T03:31:57Z-
dc.date.issued2008en_HK
dc.identifier.citationProceedings of the VLDB Endowment, 2008, v. 1, p. 722-735-
dc.identifier.issn2150-8097-
dc.identifier.urihttp://hdl.handle.net/10722/61148-
dc.description.abstractUncertain or imprecise data are pervasive in applications like location-based services, sensor monitoring, and data collection and integration. For these applications, probabilistic databases can be used to store uncertain data, and querying facilities are provided to yield answers with statistical confidence. Given that a limited amount of resources is available to “clean” the database (e.g., by probing some sensor data values to get their latest values), we address the problem of choosing the set of uncertain objects to be cleaned, in order to achieve the best improvement in the quality of query answers. For this purpose, we present the PWS-quality metric, which is a universal measure that quantifies the ambiguity of query answers under the possible world semantics. We study how PWS-quality can be efficiently evaluated for two major query classes: (1) queries that examine the satisfiability of tuples independent of other tuples (e.g., range queries); and (2) queries that require the knowledge of the relative ranking of the tuples (e.g., MAX queries). We then propose a polynomial-time solution to achieve an optimal improvement in PWS-quality. Other fast heuristics are presented as well. Experiments, performed on both real and synthetic datasets, show that the PWS-quality metric can be evaluated quickly, and that our cleaning algorithm provides an optimal solution with high efficiency. To our best knowledge, this is the first work that develops a quality metric for a probabilistic database, and investigates how such a metric can be used for data cleaning purposes.-
dc.languageengen_HK
dc.publisherVery Large Data Base (VLDB) Endowment Inc.. The Journal's web site is located at http://vldb.org/pvldb/index.html-
dc.relation.ispartofProceedings of the VLDB Endowment-
dc.titleCleaning uncertain data with quality guaranteesen_HK
dc.typeArticleen_HK
dc.identifier.emailCheng, CK: ckcheng@cs.hku.hken_HK
dc.identifier.authorityCheng, CK=rp00074en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.14778/1453856.1453935-
dc.identifier.scopuseid_2-s2.0-61349087255-
dc.identifier.hkuros150618en_HK
dc.identifier.volume1-
dc.identifier.spage722-
dc.identifier.epage735-

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