The clustering property of corner transformation for spatial database applications

Abstract

Spatial access methods (SAMs) are often used as clustering indexes in spatial database systems. Therefore, a SAM should have the clustering property both in the index and in the data file. In this paper, we argue that corner transformation preserves the clustering property such that objects having similar sizes and positions in the original space tend to be placed in the same region in the transform space. We then show that SAMs based on corner transformation are able to maintain clustering both in the index and in the data file for storage systems with fixed object positions and propose the MBR-MLGF as an example to implement such an index. In the storage systems with fixed object positions, the inserted objects never move during the operation of the system. Most storage systems currently available adopt this architecture. Extensive experiments comparing with the R ∗-tree show that corner transformation indeed preserves the clustering property, and therefore, it can be used as a useful method for spatial query processing. This result reverses the common belief that transformation will adversely affect the clustering and shows that the transformation maintains as good clustering in the transform space as conventional techniques, such as the R ∗-tree, do in the original space.