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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 796-807
Reynold Cheng , Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong
Xike Xie , Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong
Man Lung Yiu , Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong
Jinchuan Chen , Key Lab for Data Engineering and Knowledge Engineering, MOE. Renmin University of China, China
Liwen Sun , Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong
ABSTRACT
The Voronoi diagram is an important technique for answering nearest-neighbor queries for spatial databases. In this paper, we study how the Voronoi diagram can be used on uncertain data, which are inherent in scientific and business applications. In particular, we propose the Uncertain-Voronoi Diagram (or UV-diagram in short). Conceptually, the data space is divided into distinct “UV-partitions”, where each UV-partition P is associated with a set S of objects; any point q located in P has the set S as its nearest neighbor with non-zero probabilities. The UV-diagram facilitates queries that inquire objects for having non-zero chances of being the nearest neighbor of a given query point. It also allows analysis of nearest neighbor information, e.g., finding out how many objects are the nearest neighbors in a given area. However, a UV-diagram requires exponential construction and storage costs. To tackle these problems, we devise an alternative representation for UV-partitions, and develop an adaptive index for the UV-diagram. This index can be constructed in polynomial time. We examine how it can be extended to support other related queries. We also perform extensive experiments to validate the effectiveness of our approach.
CITATION
Reynold Cheng, Xike Xie, Man Lung Yiu, Jinchuan Chen, Liwen Sun, "UV-diagram: A Voronoi diagram for uncertain data", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 796-807, doi:10.1109/ICDE.2010.5447917
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