Identifying top k dominating objects over uncertain data

Zhan, L; Zhang, Y; Zhang, W; Lin, X

Identifying top k dominating objects over uncertain data

Zhan, L Zhang, Y

Zhang, W

Lin, X

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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), 2014, 8421 LNCS (PART 1), pp. 388 - 405
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Zhan, L	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Zhang, W https://orcid.org/0000-0001-6572-2600	en_US
dc.contributor.author	Lin, X https://orcid.org/0000-0003-2396-7225	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8421 LNCS (PART 1), pp. 388 - 405	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32990
dc.description.abstract	Uncertainty is inherent in many important applications, such as data integration, environmental surveillance, location-based services (LBS), sensor monitoring and radio-frequency identification (RFID). In recent years, we have witnessed significant research efforts devoted to producing probabilistic database management systems, and many important queries are re-investigated in the context of uncertain data models. In the paper, we study the problem of top k dominating query on multi-dimensional uncertain objects, which is an essential method in the multi-criteria decision analysis when an explicit scoring function is not available. Particularly, we formally introduce the top k dominating model based on the state-of-the-art top k semantic over uncertain data. We also propose effective and efficient algorithms to identify the top k dominating objects. Novel pruning techniques are proposed by utilizing the spatial indexing and statistic information, which significantly improve the performance of the algorithms in terms of CPU and I/O costs. Comprehensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of our techniques. © 2014 Springer International Publishing Switzerland.	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	10.1007/978-3-319-05810-8_26	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Identifying top k dominating objects over uncertain data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 1	en_US
utslib.citation.volume	8421 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/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	PART 1	en_US
pubs.publication-status	Published	en_US
pubs.volume	8421 LNCS	en_US

Abstract:

Uncertainty is inherent in many important applications, such as data integration, environmental surveillance, location-based services (LBS), sensor monitoring and radio-frequency identification (RFID). In recent years, we have witnessed significant research efforts devoted to producing probabilistic database management systems, and many important queries are re-investigated in the context of uncertain data models. In the paper, we study the problem of top k dominating query on multi-dimensional uncertain objects, which is an essential method in the multi-criteria decision analysis when an explicit scoring function is not available. Particularly, we formally introduce the top k dominating model based on the state-of-the-art top k semantic over uncertain data. We also propose effective and efficient algorithms to identify the top k dominating objects. Novel pruning techniques are proposed by utilizing the spatial indexing and statistic information, which significantly improve the performance of the algorithms in terms of CPU and I/O costs. Comprehensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of our techniques. © 2014 Springer International Publishing Switzerland.

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http://hdl.handle.net/10453/32990