Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

Computer and Information Science

RAS ID

4274

Comments

This article was originally published as: Xiao, J. (2006). A Comparison of Heuristics for Scheduling Spatial Clusters to Reduce I/O Cost in Spatial Join Processing. Proceedings of International Conference on Machine Learning and Cybernetics. (pp. 2455-2460). Dalian, China. IEEE. Original article available here

© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Abstract

In spatial join processing, a common method to minimize the I/O cost is to partition the spatial objects into clusters, and then to schedule the processing of the clusters in the spatial join processing such that the number of times the same objects to be fetched into memory can be minimized. A key issue of this clustering-and-scheduling approach is how to produce a better sequence of clusters to guide the cluster scheduling thus to reduce the total I/O cost of spatial join processing. This paper describes three cluster sequencing heuristics. An extensive comparison among them has been conducted, and simulation results have shown that, while using the cluster sequences generated to guide the cluster scheduling can significant reduce the I/O cost in fetching spatial objects in spatial join processing, their performance differs

DOI

10.1109/ICMLC.2006.258779

Access Rights

free_to_read

 
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Link to publisher version (DOI)

10.1109/ICMLC.2006.258779