Document Type

Conference Proceeding

Publisher

IEEE Computer Society Press

Faculty

Computing, Health and Science

School

Computer and Information Science

RAS ID

3072

Comments

This conference paper was originally published as: Xiao, J. (2006). Clustering Spatial Data for Join Operations Using Match-based Partition. Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation. (pp. 471-476). Vienna, Austria. IEEE Computer Society Press. Original article available here

Abstract

The spatial join is an operation that combines two sets of spatial data by their spatial relationships. The cost of spatial join could be very high due to the large sizes of spatial objects and the computation-intensive spatial operations. In spatial join processing, a common method to minimize the I/O cost is to partition the spatial objects into clusters and then schedule the processing of the clusters such that the number of times the same objects to be fetched into memory can be minimized. In this paper, we propose a match-based approach to partition a large spatial data set into clusters, which is computed based on the maximal match on the spatial join graph. Simulations have been conducted and the results have shown that, when comparing to existing approaches, our new method can significantly reduce the number of clusters produced in spatial join processing

DOI

10.1109/CIMCA.2005.1631513

 
COinS
 

Link to publisher version (DOI)

10.1109/CIMCA.2005.1631513