Spatial join processing using corner transformation

• Published in: IEEE transactions on knowledge and data engineering Vol. 11; no. 4; pp. 688 - 695
• Main Authors: SONG, J.-W, WHANG, K.-Y, LEE, Y.-K, LEE, M.-J, KIM, S.-W
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.1999; IEEE Computer Society
• Subjects: Algorithms; Applied sciences; Categories; Clustering algorithms; Computer science; control theory; systems; Corners; Costs; Database systems; Disks; Exact sciences and technology; Geographic Information Systems; Information systems. Data bases; Memory organisation. Data processing; Multidimensional systems; Parallel processing; Partitioning algorithms; Shape; Software; Spatial databases; Transformations; Transforms; Performance evaluation; Database; Geographic information system
• ISSN: 1041-4347
• DOI: 10.1109/69.790844

Spatial join finds pairs of spatial objects having a specific spatial relationship in spatial database systems. Since spatial join is a fairly expensive operation, we need an efficient algorithm taking advantage of the characteristics of available spatial access methods. In this paper, we propose a spatial join algorithm using corner transformation and show its excellence through experiments. To the extent of authors' knowledge, the spatial join processing using corner transformation is new. In corner transformation, two regions in one file joined with two adjacent regions in the other file share a large common area. The proposed algorithm utilizes this property in order to reduce the number of disk accesses for spatial join. Experimental results show that the performance of the algorithm is generally better than that of the R*-tree based algorithm proposed by Brinkhoff et al. (1993. 1994). This is a strong indication that corner transformation is a promising category of spatial access methods 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 effect the clustering. We also briefly mention that the join algorithm based on corner transformation has a nice property of being amenable to parallel processing. We believe that our result will provide a new insight towards transformation-based processing of spatial operations.