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Conference Paper: Quality-aware probing of uncertain data with resource constraints

TitleQuality-aware probing of uncertain data with resource constraints
Authors
Issue Date2008
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2008, v. 5069 LNCS, p. 491-508 How to Cite?
AbstractIn applications like sensor network monitoring and location-based services, due to limited network bandwidth and battery power, a system cannot always acquire accurate and fresh data from the external environment. To capture data errors in these environments, recent researches have proposed to model uncertainty as a probability distribution function (pdf), as well as the notion of probabilistic queries, which provide statistical guarantees on answer correctness. In this paper, we present an entropy-based metric to quantify the degree of ambiguity of probabilistic query answers due to data uncertainty. Based on this metric, we develop a new method to improve the query answer quality. The main idea of this method is to acquire (or probe) data from a selected set of sensing devices, in order to reduce data uncertainty and improve the quality of a query answer. Given that a query is assigned a limited number of probing resources, we investigate how the quality of a query answer can attain an optimal improvement. To improve the efficiency of our solution, we further present heuristics which achieve near-to-optimal quality improvement. We generalize our solution to handle multiple queries. An experimental simulation over a realistic dataset is performed to validate our approaches. © 2008 Springer-Verlag.
Description20th Intl. Conf. on Scientific and Statistical Database Management (SSDBM 2008), Hong Kong
Persistent Identifierhttp://hdl.handle.net/10722/61151
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorChen, Jen_HK
dc.contributor.authorCheng, Ren_HK
dc.date.accessioned2010-07-13T03:32:02Z-
dc.date.available2010-07-13T03:32:02Z-
dc.date.issued2008en_HK
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2008, v. 5069 LNCS, p. 491-508en_HK
dc.identifier.isbn978-3-540-69497-7en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/61151-
dc.description20th Intl. Conf. on Scientific and Statistical Database Management (SSDBM 2008), Hong Kongen_HK
dc.description.abstractIn applications like sensor network monitoring and location-based services, due to limited network bandwidth and battery power, a system cannot always acquire accurate and fresh data from the external environment. To capture data errors in these environments, recent researches have proposed to model uncertainty as a probability distribution function (pdf), as well as the notion of probabilistic queries, which provide statistical guarantees on answer correctness. In this paper, we present an entropy-based metric to quantify the degree of ambiguity of probabilistic query answers due to data uncertainty. Based on this metric, we develop a new method to improve the query answer quality. The main idea of this method is to acquire (or probe) data from a selected set of sensing devices, in order to reduce data uncertainty and improve the quality of a query answer. Given that a query is assigned a limited number of probing resources, we investigate how the quality of a query answer can attain an optimal improvement. To improve the efficiency of our solution, we further present heuristics which achieve near-to-optimal quality improvement. We generalize our solution to handle multiple queries. An experimental simulation over a realistic dataset is performed to validate our approaches. © 2008 Springer-Verlag.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.titleQuality-aware probing of uncertain data with resource constraintsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-3-540-69497-7&volume=&spage=491&epage=508&date=2008&atitle=Quality-Aware+Probing+of+Uncertain+Data+with+Resource+Constraintsen_HK
dc.identifier.emailCheng, R:ckcheng@cs.hku.hken_HK
dc.identifier.authorityCheng, R=rp00074en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-540-69497-7_31en_HK
dc.identifier.scopuseid_2-s2.0-49049105203en_HK
dc.identifier.hkuros150621en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-49049105203&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume5069 LNCSen_HK
dc.identifier.spage491en_HK
dc.identifier.epage508en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridChen, J=23501401700en_HK
dc.identifier.scopusauthoridCheng, R=7201955416en_HK

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