Yi Choon Kun, Kim Jeung Won, Kim Young Ju, Chung Ki Dong

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 6, No. 9, pp. 2294-2304, Sep. 1999
10.3745/KIPSTE.1999.6.9.2294,   PDF Download:

Abstract

Our research purpose in this paper is to improve the performance of query processing in GIS(Geographic Information System) by enhancing the I/O performance exploiting parallel I/O and efficient disk access. By packing adjacent spatial data, which are very likely to be referenced concurrently, into one block or continuous disk blocks, the number of disk accesses and the disk access overhead for query processing can be decreased, and this eventually leads to the I/O time decrease. So, in this paper, we proposes EPR(Enhanced Parallel R-tree) indexing method which integrates the parallel I/O method of the previous Parallel R-tree method and a packing-based clustering method. The major characteristics of EPR method are as follows. First, EPR method arranges spatial data in the increasing order of proximity by using Hilbert space filling curve, and builds a packet R-tree by bottom-up manner. Second, with packing-based clustering in which arranged spatial data are clustered into continuous disk blocks, EPR method generates spatial data clusters. Third, EPR method distributes EPR index nodes and spatial data clusters on multiple disks through round-robin striping. Experimental results show that EPR method achieves up to 30% or more gains over PR method in query processing speed. In particular, the larger the size of disk blocks is and the smaller the size of spatial data objects is, the better the performance of query processing by EPR method is.

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