Probabilistic top-k dominating query over sliding windows

Feng, X; Zhao, X; Gao, Y; Zhang, Y

Probabilistic top-k dominating query over sliding windows

Feng, X Zhao, X Gao, Y Zhang, Y

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7808 LNCS pp. 782 - 793
Issue Date:: 2013-04-10

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Field	Value	Language
dc.contributor.author	Feng, X	en_US
dc.contributor.author	Zhao, X	en_US
dc.contributor.author	Gao, Y	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.date.issued	2013-04-10	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7808 LNCS pp. 782 - 793	en_US
dc.identifier.isbn	9783642374005	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120828
dc.description.abstract	Probabilistic queries on uncertain data have been intensively investigated lately. Top-k dominating query is important in many applications, e.g., decision making, as it offers choices which are better than the most of others. In this paper, we study the problem of probabilistic top-k dominating query over sliding windows. An efficient algorithm is developed to compute the exact solution. Extensive experiments are conducted to demonstrate the efficiency and effectiveness of the algorithm. © 2013 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	10.1007/978-3-642-37401-2_75	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Probabilistic top-k dominating query over sliding windows	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7808 LNCS	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	080109 Pattern Recognition and Data Mining	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.publication-status	Published	en_US
pubs.volume	7808 LNCS	en_US

Abstract:

Probabilistic queries on uncertain data have been intensively investigated lately. Top-k dominating query is important in many applications, e.g., decision making, as it offers choices which are better than the most of others. In this paper, we study the problem of probabilistic top-k dominating query over sliding windows. An efficient algorithm is developed to compute the exact solution. Extensive experiments are conducted to demonstrate the efficiency and effectiveness of the algorithm. © 2013 Springer-Verlag.

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http://hdl.handle.net/10453/120828