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Conference Paper: Evaluating Probabilistic Queries over Imprecise Data

TitleEvaluating Probabilistic Queries over Imprecise Data
Authors
Issue Date2003
PublisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://www.acm.org/sigmod
Citation
Proceedings Of The Acm Sigmod International Conference On Management Of Data, 2003, p. 551-562 How to Cite?
AbstractMany applications employ sensors for monitoring entities such as temperature and wind speed. A centralized database tracks these entities to enable query processing. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), it is often infeasible to store the exact values at all times. A similar situation exists for moving object environments that track the constantly changing locations of objects. In this environment, it is possible for database queries to produce incorrect or invalid results based upon old data. However, if the degree of error (or uncertainty) between the actual value and the database value is controlled, one can place more confidence in the answers to queries. More generally, query answers can be augmented with probabilistic estimates of the validity of the answers. In this paper we study probabilistic query evaluation based upon 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. 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.
Persistent Identifierhttp://hdl.handle.net/10722/151847
ISSN

 

DC FieldValueLanguage
dc.contributor.authorCheng, Ren_US
dc.contributor.authorKalashnikov, DVen_US
dc.contributor.authorPrabhakar, Sen_US
dc.date.accessioned2012-06-26T06:30:02Z-
dc.date.available2012-06-26T06:30:02Z-
dc.date.issued2003en_US
dc.identifier.citationProceedings Of The Acm Sigmod International Conference On Management Of Data, 2003, p. 551-562en_US
dc.identifier.issn0730-8078en_US
dc.identifier.urihttp://hdl.handle.net/10722/151847-
dc.description.abstractMany applications employ sensors for monitoring entities such as temperature and wind speed. A centralized database tracks these entities to enable query processing. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), it is often infeasible to store the exact values at all times. A similar situation exists for moving object environments that track the constantly changing locations of objects. In this environment, it is possible for database queries to produce incorrect or invalid results based upon old data. However, if the degree of error (or uncertainty) between the actual value and the database value is controlled, one can place more confidence in the answers to queries. More generally, query answers can be augmented with probabilistic estimates of the validity of the answers. In this paper we study probabilistic query evaluation based upon 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. 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.en_US
dc.languageengen_US
dc.publisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://www.acm.org/sigmoden_US
dc.relation.ispartofProceedings of the ACM SIGMOD International Conference on Management of Dataen_US
dc.titleEvaluating Probabilistic Queries over Imprecise Dataen_US
dc.typeConference_Paperen_US
dc.identifier.emailCheng, R:ckcheng@cs.hku.hken_US
dc.identifier.authorityCheng, R=rp00074en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-1142291577en_US
dc.identifier.spage551en_US
dc.identifier.epage562en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridCheng, R=7201955416en_US
dc.identifier.scopusauthoridKalashnikov, DV=6602598174en_US
dc.identifier.scopusauthoridPrabhakar, S=7101672592en_US

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