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Conference Paper: Efficient evaluation of imprecise location-dependent queries

TitleEfficient evaluation of imprecise location-dependent queries
Authors
Issue Date2007
PublisherIEEE, Computer Society.
Citation
The IEEE 23rd International Conference on Data Engineering (ICDE 2007), Istanbul, Turkey, 15-20 April 2007. In International Conference on Data Engineering. Proceedings, 2007, p. 586-595 How to Cite?
AbstractIn location-based services, it is common for a user to issue a query based on his/her current position. One such example is "find the available cabs within two miles of my current location". Very often, the query issuers' locations are imprecise due to measurement error, sampling error, or message delay. They may also want to protect their privacy by providing a less precise location. In this paper, we study the efficiency of queries that return probabilistic guarantees for location data with uncertainty. We classify this query into two types, based on whether the data (1) has no uncertainty (e.g., shops and restaurants), or (2) has a controlled degree of uncertainty (e.g., moving vehicles). Based on this classification, we develop three methods to improve the computational and I/O performance. The first method expands the query range based on the query issuer's uncertainty. The second idea exchanges the roles of query and data. The third technique exploits the fact that users may only be interested in answers with probabilities higher than some threshold. Experimental simulation over a realistic dataset reveals that our approaches improve the query performance significantly. © 2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/144829
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChen, Jen_HK
dc.contributor.authorCheng, Ren_HK
dc.date.accessioned2012-02-07T08:40:42Z-
dc.date.available2012-02-07T08:40:42Z-
dc.date.issued2007en_HK
dc.identifier.citationThe IEEE 23rd International Conference on Data Engineering (ICDE 2007), Istanbul, Turkey, 15-20 April 2007. In International Conference on Data Engineering. Proceedings, 2007, p. 586-595en_HK
dc.identifier.issn1084-4627en_HK
dc.identifier.urihttp://hdl.handle.net/10722/144829-
dc.description.abstractIn location-based services, it is common for a user to issue a query based on his/her current position. One such example is "find the available cabs within two miles of my current location". Very often, the query issuers' locations are imprecise due to measurement error, sampling error, or message delay. They may also want to protect their privacy by providing a less precise location. In this paper, we study the efficiency of queries that return probabilistic guarantees for location data with uncertainty. We classify this query into two types, based on whether the data (1) has no uncertainty (e.g., shops and restaurants), or (2) has a controlled degree of uncertainty (e.g., moving vehicles). Based on this classification, we develop three methods to improve the computational and I/O performance. The first method expands the query range based on the query issuer's uncertainty. The second idea exchanges the roles of query and data. The third technique exploits the fact that users may only be interested in answers with probabilities higher than some threshold. Experimental simulation over a realistic dataset reveals that our approaches improve the query performance significantly. © 2007 IEEE.en_HK
dc.languageeng-
dc.publisherIEEE, Computer Society.-
dc.relation.ispartofInternational Conference on Data Engineering. Proceedingsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleEfficient evaluation of imprecise location-dependent queriesen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailCheng, R:ckcheng@cs.hku.hken_HK
dc.identifier.authorityCheng, R=rp00074en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDE.2007.367904en_HK
dc.identifier.scopuseid_2-s2.0-34548740732en_HK
dc.identifier.hkuros176471-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34548740732&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage586en_HK
dc.identifier.epage595en_HK
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe IEEE 23rd International Conference on Data Engineering (ICDE 2007), Istanbul, Turkey, 15-20 April 2007. In International Conference on Data Engineering. Proceedings, 2007, p. 586-595-
dc.identifier.scopusauthoridChen, J=23501401700en_HK
dc.identifier.scopusauthoridCheng, R=7201955416en_HK

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