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Issue No. 07 - July (2012 vol. 24)
ISSN: 1041-4347
pp: 1244-1258
Ying Zhang , University of New South Wales, Sydney
Xuemin Lin , University of New South Wales, Sydney
Yufei Tao , Chinese University of Hong Kong, Hong Kong
Wenjie Zhang , University of New South Wales, Sydney
Haixun Wang , Microsoft Research, Beijing
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^{\prime } of S such that for each p \in S^{\prime }, Q has at least probability \theta 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.
Uncertainty, index, range aggregate query.

H. Wang, Y. Zhang, W. Zhang, Y. Tao and X. Lin, "Efficient Computation of Range Aggregates against Uncertain Location-Based Queries," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 1244-1258, 2011.
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