Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks

Cheema, MA; Zhang, W; Lin, X; Zhang, Y; Li, X

Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks

Cheema, MA Zhang, W Lin, X Zhang, Y

Li, X

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Publication Type:: Journal Article
Citation:: VLDB Journal, 2012, 21 (1), pp. 69 - 95
Issue Date:: 2012-02-01

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Field	Value	Language
dc.contributor.author	Cheema, MA	en_US
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Lin, X	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2012-02-01	en_US
dc.identifier.citation	VLDB Journal, 2012, 21 (1), pp. 69 - 95	en_US
dc.identifier.issn	1066-8888	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30922
dc.description.abstract	In this paper, we study the problem of continuous monitoring of reverse k nearest neighbors queries in Euclidean space as well as in spatial networks. Existing techniques are sensitive toward objects and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We present a framework for continuous reverse k nearest neighbor (RkNN) queries by assigning each object and query with a safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions. This significantly improves the computation cost. As a byproduct, our framework also reduces the communication cost in client-server architectures because an object does not report its location to the server unless it leaves its safe region or the server sends a location update request. We also conduct a rigid cost analysis for our Euclidean space RkNN algorithm. We show that our techniques can also be applied to answer bichromatic RkNN queries in Euclidean space as well as in spatial networks. Furthermore, we show that our techniques can be extended for the spatial networks that are represented by directed graphs. The extensive experiments demonstrate that our techniques outperform the existing techniques by an order of magnitude in terms of computation cost and communication cost. © 2011 Springer-Verlag.	en_US
dc.relation.ispartof	VLDB Journal	en_US
dc.relation.isbasedon	10.1007/s00778-011-0235-9	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	21	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0804 Data Format	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

In this paper, we study the problem of continuous monitoring of reverse k nearest neighbors queries in Euclidean space as well as in spatial networks. Existing techniques are sensitive toward objects and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We present a framework for continuous reverse k nearest neighbor (RkNN) queries by assigning each object and query with a safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions. This significantly improves the computation cost. As a byproduct, our framework also reduces the communication cost in client-server architectures because an object does not report its location to the server unless it leaves its safe region or the server sends a location update request. We also conduct a rigid cost analysis for our Euclidean space RkNN algorithm. We show that our techniques can also be applied to answer bichromatic RkNN queries in Euclidean space as well as in spatial networks. Furthermore, we show that our techniques can be extended for the spatial networks that are represented by directed graphs. The extensive experiments demonstrate that our techniques outperform the existing techniques by an order of magnitude in terms of computation cost and communication cost. © 2011 Springer-Verlag.

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http://hdl.handle.net/10453/30922