Monitoring aggregate k-NN objects in road networks

Qin, L; Yu, JX; Ding, B; Ishikawa, Y

Monitoring aggregate k-NN objects in road networks

Qin, L

Yu, JX

Ding, B Ishikawa, Y

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Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 5069 LNCS pp. 168 - 186
Issue Date:: 2008-01-01

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Field	Value	Language
dc.contributor.author	Qin, L https://orcid.org/0000-0001-6068-5062	en_US
dc.contributor.author	Yu, JX https://orcid.org/0000-0002-9738-827X	en_US
dc.contributor.author	Ding, B	en_US
dc.contributor.author	Ishikawa, Y	en_US
dc.date.issued	2008-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 5069 LNCS pp. 168 - 186	en_US
dc.identifier.isbn	9783540694762	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28890
dc.description.abstract	In recent years, there is an increasing need to monitor k nearest neighbor (k-NN) in a road network. There are existing solutions on either monitoring k-NN objects from a single query point over a road network, or computing the snapshot k-NN objects over a road network to minimize an aggregate distance function with respect to multiple query points. In this paper, we study a new problem that is to monitor k-NN objects over a road network from multiple query points to minimize an aggregate distance function with respect to the multiple query points. We call it a continuous aggregate k-NN (CANN) query. We propose a new approach that can significantly reduce the cost of computing network distances when monitoring aggregate k-NN objects on road networks. We conducted extensive experimental studies and confirmed the efficiency of our algorithms. © 2008 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-540-69497-7_13	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Monitoring aggregate k-NN objects in road networks	en_US
dc.type	Journal Article
utslib.citation.volume	5069 LNCS	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Hong Kong, China	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	5069 LNCS	en_US

Abstract:

In recent years, there is an increasing need to monitor k nearest neighbor (k-NN) in a road network. There are existing solutions on either monitoring k-NN objects from a single query point over a road network, or computing the snapshot k-NN objects over a road network to minimize an aggregate distance function with respect to multiple query points. In this paper, we study a new problem that is to monitor k-NN objects over a road network from multiple query points to minimize an aggregate distance function with respect to the multiple query points. We call it a continuous aggregate k-NN (CANN) query. We propose a new approach that can significantly reduce the cost of computing network distances when monitoring aggregate k-NN objects on road networks. We conducted extensive experimental studies and confirmed the efficiency of our algorithms. © 2008 Springer-Verlag.

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http://hdl.handle.net/10453/28890