Probabilistic skyline operator over sliding windows

Zhang, W; Lin, X; Zhang, Y; Wang, W; Yu, JX

Probabilistic skyline operator over sliding windows

Zhang, W Lin, X Zhang, Y

Wang, W Yu, JX

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Data Engineering, 2009, pp. 1060 - 1071
Issue Date:: 2009-07-08

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Field	Value	Language
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	Wang, W	en_US
dc.contributor.author	Yu, JX https://orcid.org/0000-0002-9738-827X	en_US
dc.date.issued	2009-07-08	en_US
dc.identifier.citation	Proceedings - International Conference on Data Engineering, 2009, pp. 1060 - 1071	en_US
dc.identifier.isbn	9780769535456	en_US
dc.identifier.issn	1084-4627	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28966
dc.description.abstract	Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of efficient processing of continuous skyline queries over sliding windows on uncertain data elements regarding given probability thresholds. We first characterize what kind of elements we need to keep in our query computation. Then we show the size of dynamically maintained candidate set and the size of skyline. We develop novel, efficient techniques to process a continuous, probabilistic skyline query. Finally, we extend our techniques to the applications where multiple probability thresholds are given or we want to retrieve "top-k" skyline data objects. Our extensive experiments demonstrate that the proposed techniques are very efficient and handle a high-speed data stream in real time. © 2009 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Data Engineering	en_US
dc.relation.isbasedon	10.1109/ICDE.2009.83	en_US
dc.title	Probabilistic skyline operator over sliding windows	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
dc.location.activity	Shanghai, 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/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

Abstract:

Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of efficient processing of continuous skyline queries over sliding windows on uncertain data elements regarding given probability thresholds. We first characterize what kind of elements we need to keep in our query computation. Then we show the size of dynamically maintained candidate set and the size of skyline. We develop novel, efficient techniques to process a continuous, probabilistic skyline query. Finally, we extend our techniques to the applications where multiple probability thresholds are given or we want to retrieve "top-k" skyline data objects. Our extensive experiments demonstrate that the proposed techniques are very efficient and handle a high-speed data stream in real time. © 2009 IEEE.

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http://hdl.handle.net/10453/28966