Efficient computation of range aggregates against uncertain location-based queries

Zhang, Y; Lin, X; Tao, Y; Zhang, W; Wang, H

Efficient computation of range aggregates against uncertain location-based queries

Zhang, Y

Lin, X Tao, Y Zhang, W Wang, H

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2012, 24 (7), pp. 1244 - 1258
Issue Date:: 2012-06-06

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Lin, X	en_US
dc.contributor.author	Tao, Y	en_US
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Wang, H	en_US
dc.date.issued	2012-06-06	en_US
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2012, 24 (7), pp. 1244 - 1258	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28919
dc.description.abstract	In many applications, including location-based services, queries may not be precise. In this paper, we study the problem of efficiently computing range aggregates in a multidimensional space when the query location is uncertain. Specifically, for a query point Q whose location is uncertain and a set S of points in a multidimensional space, we want to calculate the aggregate (e.g., count, average and sum) over the subset S′ of S such that for each p ∈ S′, Q has at least probability θ within the distance \gamma to p. We propose novel, efficient techniques to solve the problem following the filtering-and-verification paradigm. In particular, two novel filtering techniques are proposed to effectively and efficiently remove data points from verification. Our comprehensive experiments based on both real and synthetic data demonstrate the efficiency and scalability of our techniques. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2011.46	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Efficient computation of range aggregates against uncertain location-based queries	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	24	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	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	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

In many applications, including location-based services, queries may not be precise. In this paper, we study the problem of efficiently computing range aggregates in a multidimensional space when the query location is uncertain. Specifically, for a query point Q whose location is uncertain and a set S of points in a multidimensional space, we want to calculate the aggregate (e.g., count, average and sum) over the subset S′ of S such that for each p ∈ S′, Q has at least probability θ within the distance \gamma to p. We propose novel, efficient techniques to solve the problem following the filtering-and-verification paradigm. In particular, two novel filtering techniques are proposed to effectively and efficiently remove data points from verification. Our comprehensive experiments based on both real and synthetic data demonstrate the efficiency and scalability of our techniques. © 2012 IEEE.

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http://hdl.handle.net/10453/28919