AVR-Tree: Speeding Up the NN and ANN Queries on Location Data
نویسندگان
چکیده
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.
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