AVR-tree: Speeding up the NN and ANN queries on location data

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7825 LNCS (PART 1), pp. 116 - 130
Issue Date:
2013-12-01
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In the paper, we study the problems of nearest neighbor queries (NN) and all nearest neighbor queries (ANN) on location data, which have a wide range of applications such as Geographic Information System (GIS) and Location based Service (LBS). We propose a new structure, termed AVR-Tree, based on the R-tree and Voronoi diagram techniques. Compared with the existing indexing techniques used for NN and ANN queries on location data, AVR-Tree can achieve a better tradeoff between the pruning effectiveness and the index size for NN and ANN queries. We also conduct a comprehensive performance evaluation for the proposed techniques based on both real and synthetic data, which shows that AVR-Tree based NN and ANN algorithms achieve better performance compared with their best competitors in terms of both CPU and I/O costs. © Springer-Verlag 2013.
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