Transformation-based spatial join

  • Authors:

  • Ju-Won Song;Kyu-Young Whang;Young-Koo Lee;Min-Jae Lee;Sang-Wook Kim

  • Affiliations:

  • Multimedia Technology Research Lab, Korea Telecom, 17 Woomyon-dong, Suchcho-gu, Seoul, 137-792, Korea;Department of Computer Science, Advanced Information Technology Research Center, Korea Advanced Institute of Science and Technology;Department of Computer Science, Advanced Information Technology Research Center, Korea Advanced Institute of Science and Technology;-;Department of Information and Telecommunications Engineering, Kangwon National University

  • Venue:
  • Proceedings of the eighth international conference on Information and knowledge management
  • Year:
  • 1999

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Abstract

Spatial join finds pairs of spatial objects having a specific spatial relationship in spatial database systems. A number of spatial join algorithms have recently been proposed in the literature. Most of them, however, perform the join in the original space. Joining in the original space has a drawback of dealing with sizes of objects and thus has difficulty in developing a formal algorithm that does not rely on heuristics. In this paper, we propose a spatial join algorithm based on the transformation technique. An object having a size in the two-dimensional original space is transformed into a point in the four-dimensional transform space, and the join is performed on these point objects. This can be easily extended to n-dimensional cases. We show the excellence of the proposed approach through analysis and extensive experiments. The results show that the proposed algorithm has a performance generally better than that of the R*-based algorithm proposed by Brinkhoff et al. This is a strong indicating that corner transformation preserves clustering among objects and that spatial operations can be performed better in the transform space than in the original space. This reverses the common belief that transformation will adversely affect clustering. We believe that our result will provide a new insight towards transformation-based spatial query processing.