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Conference Paper: Probabilistic verifiers: evaluating constrained nearest-neighbor queries over uncertain data

TitleProbabilistic verifiers: evaluating constrained nearest-neighbor queries over uncertain data
Authors
Issue Date2008
Citation
The 24th IEEE International Conference on Data Engineering (ICDE 2008), Canaun, Mexico, 7-12 April 2008. In Conference Proceedings, 2008, p. 973-982 How to Cite?
AbstractIn applications like location-based services, sensor monitoring and biological databases, the values of the database items are inherently uncertain in nature. An important query for uncertain objects is the Probabilistic Nearest-Neighbor Query (PNN), which computes the probability of each object for being the nearest neighbor of a query point. Evaluating this query is computationally expensive, since it needs to consider the relationship among uncertain objects, and requires the use of numerical integration or Monte-Carlo methods. Sometimes, a query user may not be concerned about the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Constrained Nearest-Neighbor Query (C-PNN), which returns the IDs of objects whose probabilities are higher than some threshold, with a given error bound in the answers. The C-PNN can be answered efficiently with probabilistic verifiers. These are methods that derive the lower and upper bounds of answer probabilities, so that an object can be quickly decided on whether it should be included in the answer. We have developed three probabilistic verifiers, which can be used on uncertain data with arbitrary probability density functions. Extensive experiments were performed to examine the effectiveness of these approaches. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/151929
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChengt, Ren_US
dc.contributor.authorChen, JCen_US
dc.contributor.authorMokbel, Men_US
dc.contributor.authorChow, CYen_US
dc.date.accessioned2012-06-26T06:31:01Z-
dc.date.available2012-06-26T06:31:01Z-
dc.date.issued2008en_US
dc.identifier.citationThe 24th IEEE International Conference on Data Engineering (ICDE 2008), Canaun, Mexico, 7-12 April 2008. In Conference Proceedings, 2008, p. 973-982en_US
dc.identifier.isbn978-1-4244-1837-4-
dc.identifier.issn1084-4627en_US
dc.identifier.urihttp://hdl.handle.net/10722/151929-
dc.description.abstractIn applications like location-based services, sensor monitoring and biological databases, the values of the database items are inherently uncertain in nature. An important query for uncertain objects is the Probabilistic Nearest-Neighbor Query (PNN), which computes the probability of each object for being the nearest neighbor of a query point. Evaluating this query is computationally expensive, since it needs to consider the relationship among uncertain objects, and requires the use of numerical integration or Monte-Carlo methods. Sometimes, a query user may not be concerned about the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Constrained Nearest-Neighbor Query (C-PNN), which returns the IDs of objects whose probabilities are higher than some threshold, with a given error bound in the answers. The C-PNN can be answered efficiently with probabilistic verifiers. These are methods that derive the lower and upper bounds of answer probabilities, so that an object can be quickly decided on whether it should be included in the answer. We have developed three probabilistic verifiers, which can be used on uncertain data with arbitrary probability density functions. Extensive experiments were performed to examine the effectiveness of these approaches. © 2008 IEEE.en_US
dc.languageengen_US
dc.relation.ispartofIEEE International Conference on Data Engineeringen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleProbabilistic verifiers: evaluating constrained nearest-neighbor queries over uncertain dataen_US
dc.typeConference_Paperen_US
dc.identifier.emailChengt, R:ckcheng@cs.hku.hken_US
dc.identifier.authorityChengt, R=rp00074en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDE.2008.4497506en_US
dc.identifier.scopuseid_2-s2.0-52649165058en_US
dc.identifier.hkuros150626-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-52649165058&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage973en_US
dc.identifier.epage982en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChengt, R=7201955416en_US
dc.identifier.scopusauthoridChen, J=36692766900en_US
dc.identifier.scopusauthoridMokbel, M=6603620488en_US
dc.identifier.scopusauthoridChow, CY=7402578459en_US
dc.identifier.citeulike4082634-
dc.customcontrol.immutablesml 151026 - merged-

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