Yanmin Luo
ABSTRACT
Aggregate Nearest Neighbor Queries are much more complex than Nearest Neighbor queries, and pruning strategies are always utilized in ANN queries. Most of the pruning methods are based on the data index mechanisms, such as R-tree. But for the well-known curse of dimensionality, ANN search could be meaningless in high dimensional spaces. In this paper, we propose two non-index pruning strategies in ANN queries on metric space. Our methods utilize the r-NN query and projecting law, analyze the distributing of query points, find out the search region in data space, and get the result efficiently.
- Dimitris Papadias, Yufei Tao, Kyriakos Mouratidis, Chun Kit Hui: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. 30(2): 529--576 (2005) Google Scholar
Digital Library
- Hongga Li, Hua Lu, Bo Huang, Zhiyong Huang: Two ellipse-based pruning methods for group nearest neighbor queries. GIS 2005: 192--199 Google ScholarDigital Library
- Man Lung Yiu, Nikos Mamoulis, Dimitris Papadias: Aggregate Nearest Neighbor Queries in Road Networks. IEEE Trans. Knowl. Data Eng. 17(6): 820--833 (2005) Google ScholarDigital Library
Index Terms
- Finding aggregate nearest neighbor efficiently without indexing
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