Subscribe

Issue No.07 - July (2012 vol.24)

pp: 1244-1258

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

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.46

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^{\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.

INDEX TERMS

Uncertainty, index, range aggregate query.

CITATION

Xuemin Lin, Yufei Tao, Wenjie Zhang, Haixun Wang, "Efficient Computation of Range Aggregates against Uncertain Location-Based Queries",

*IEEE Transactions on Knowledge & Data Engineering*, vol.24, no. 7, pp. 1244-1258, July 2012, doi:10.1109/TKDE.2011.46REFERENCES