Efficient methods in finding aggregate nearest neighbor by projection-based filtering

  • Authors:

  • Yanmin Luo;Hanxiong Chen;Kazutaka Furuse;Nobuo Ohbo

  • Affiliations:

  • Dept. Computer Science, University of Tsukuba, Tsukuba, Ibaraki, Japan and Dept. Computer Science, HuaQiao University, QuanZhou, FuJian, China;Dept. Computer Science, University of Tsukuba, Tsukuba, Ibaraki, Japan;Dept. Computer Science, University of Tsukuba, Tsukuba, Ibaraki, Japan;Dept. Computer Science, University of Tsukuba, Tsukuba, Ibaraki, Japan

  • Venue:
  • ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
  • Year:
  • 2007

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Abstract

Aggregate Nearest Neighbor (ANN) queries developing from Nearest Neighbor (NN) queries are the relatively new query type in spatial database and data mining. An ANN queries return the object that minimizes an aggregate distance function with respect to a set of query points. Because of the multiple query points, ANN queries are much more complex than NN queries. For optimizing the query processing and improving the query efficiency, many ANN queries algorithms utilizes pruning strategies. In this paper, we propose two points projecting based ANN queries algorithms which can efficiently prune the data points without indexing. We project the query points into special "line", on which we analyses their distributing, then pruning the search space. Unlike many other algorithms based on the data index mechanisms, our algorithms avoid the curse of dimensionality and are effective and efficient in both high dimensional space and metric space. We conduct experimental studies using both real dataset and synthetic datasets to compare and evaluate their efficiencies.