NDLI: Clustering Spatial Data for Join Operations Using Match-based Partition

Content Provider	IEEE Xplore Digital Library
Author	Jitian Xiao
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. & Inf. Sci., Edith Cowan Univ., Mount Lawley, WA (Jitian Xiao)
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
Sponsorship	IEEE Comput Intelligence Soc. Eur. Soc. for Fuzzy Logic and Technol. Eur. Neural Networks Soc. Int. Assoc. for Fuzzy Set in Manage. and Economy Japan Soc. for Fuzzy Theor. and Soc. for Fuzzy Theor. and Intelligent Informatics Taiwan Fuzzy Syst. Assoc. World Wide Web Bus. Intelligence Hungarian Fuzzy Assoc. Univ. of Canberra
Starting Page	471
Ending Page	476
File Size	236355
Page Count	6
File Format	PDF
ISBN	0769525040
DOI	10.1109/CIMCA.2005.1631513
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-28
Publisher Place	Austria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information science Computational geometry Costs Filters Processor scheduling Computational modeling Intrusion detection Filtering algorithms Spatial databases Australia
Content Type	Text
Resource Type	Article

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