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Article: Evaluation of probabilistic queries over imprecise data in constantly-evolving environments

TitleEvaluation of probabilistic queries over imprecise data in constantly-evolving environments
Authors
KeywordsConstantly-evolving environments
Data caching
Data uncertainty
Entropy
Probabilistic queries
Query quality
Issue Date2007
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/is
Citation
Information Systems, 2007, v. 32 n. 1, p. 104-130 How to Cite?
AbstractSensors are often employed to monitor continuously changing entities like locations of moving objects and temperature. The sensor readings are reported to a database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), the database may not be able to keep track of the actual values of the entities. Queries that use these old values may produce incorrect answers. However, if the degree of uncertainty between the actual data value and the database value is limited, one can place more confidence in the answers to the queries. More generally, query answers can be augmented with probabilistic guarantees of the validity of the answers. In this paper, we study probabilistic query evaluation based on uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers, and provide efficient indexing and numeric solutions. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments are performed to examine the effectiveness of several data update policies. © 2005 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/129988
ISSN
2015 Impact Factor: 1.832
2015 SCImago Journal Rankings: 1.202
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCheng, Ren_HK
dc.contributor.authorKalashnikov, DVen_HK
dc.contributor.authorPrabhakar, Sen_HK
dc.date.accessioned2010-12-23T08:45:10Z-
dc.date.available2010-12-23T08:45:10Z-
dc.date.issued2007en_HK
dc.identifier.citationInformation Systems, 2007, v. 32 n. 1, p. 104-130en_HK
dc.identifier.issn0306-4379en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129988-
dc.description.abstractSensors are often employed to monitor continuously changing entities like locations of moving objects and temperature. The sensor readings are reported to a database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), the database may not be able to keep track of the actual values of the entities. Queries that use these old values may produce incorrect answers. However, if the degree of uncertainty between the actual data value and the database value is limited, one can place more confidence in the answers to the queries. More generally, query answers can be augmented with probabilistic guarantees of the validity of the answers. In this paper, we study probabilistic query evaluation based on uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers, and provide efficient indexing and numeric solutions. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments are performed to examine the effectiveness of several data update policies. © 2005 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/isen_HK
dc.relation.ispartofInformation Systemsen_HK
dc.subjectConstantly-evolving environmentsen_HK
dc.subjectData cachingen_HK
dc.subjectData uncertaintyen_HK
dc.subjectEntropyen_HK
dc.subjectProbabilistic queriesen_HK
dc.subjectQuery qualityen_HK
dc.titleEvaluation of probabilistic queries over imprecise data in constantly-evolving environmentsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0306-4379&volume=32&issue=1&spage=104&epage=130&date=2007&atitle=Evaluation+of+probabilistic+queries+over+imprecise+data+in+constantly-evolving+environments-
dc.identifier.emailCheng, R:ckcheng@cs.hku.hken_HK
dc.identifier.authorityCheng, R=rp00074en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.is.2005.06.002en_HK
dc.identifier.scopuseid_2-s2.0-33749548584en_HK
dc.identifier.hkuros176454en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33749548584&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume32en_HK
dc.identifier.issue1en_HK
dc.identifier.spage104en_HK
dc.identifier.epage130en_HK
dc.identifier.isiWOS:000242064900005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridCheng, R=7201955416en_HK
dc.identifier.scopusauthoridKalashnikov, DV=6602598174en_HK
dc.identifier.scopusauthoridPrabhakar, S=7101672592en_HK

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