An Enhanced Technique for k-Nearest Neighbor Queries with Non-Spatial Selection Predicates

  • Authors:

  • Dong-Joo Park; Hyoung-Joo Kim

  • Affiliations:

  • School of Computer Science and Engineering, Seoul National University, San 56-1, Shillim-dong, Gwanak-gu, Seoul 151-742, Korea.;School of Computer Science and Engineering, Seoul National University, San 56-1, Shillim-dong, Gwanak-gu, Seoul 151-742, Korea.

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2003

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Abstract

In multimedia databases, k-nearest neighbor queries are popular and frequently contain non-spatial predicates. Among the available techniques for such queries, the incremental nearest neighbor algorithm proposed by Hjaltason and Samet is known as the most useful algorithm [16]. The reason is that if k′ k neighbors are needed, it can provide the next neighbor for the upper operator without restarting the query from scratch. However, the R-tree in their algorithm has no facility capable of partially pruning tuple candidates that will turn out not to satisfy the remaining predicates, leading their algorithm to inefficiency. In this paper, we propose an RS-tree-based incremental nearest neighbor algorithm complementary to their algorithm. The RS-tree used in our algorithm is a hybrid of the R-tree and the S-tree, as its buddy tree, based on the hierarchical signature file. Experimental results show that our RS-tree enhances the performance of Hjaltason and Samet's algorithm.