Skewed Data Handling Technique Using an Enhanced Spatial Hash Join Algorithm


The KIPS Transactions:PartD, Vol. 12, No. 2, pp. 179-188, Apr. 2005
10.3745/KIPSTD.2005.12.2.179,   PDF Download:

Abstract

Much research for spatial join has been extensively studied over the last decade. In this paper, we focus on the filtering step of candidate objects for spatial join operations on the input tables that none of the inputs is indexed. In this case, many algorithms has presented and showed excellent performance over most spatial data. However, if data sets of input table for the spatial join are skewed, the join performance is dramatically degraded. Also, little research on solving the problem in the presence of skewed data has been attempted. Therefore, we propose a spatial hash strip join (SHSJ) algorithm that combines properties of the existing spatial hash join (SHJ) algorithm based on spatial partition for input data set's distribution and SSSJ algorithm. Finally, in order to show SHSJ the outperform in uniform/skew cases, we experiment SHSJ using the Tiger/line data sets and compare it with the SHJ algorithm.


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Cite this article
[IEEE Style]
Y. B. Shim and J. Y. Lee, "Skewed Data Handling Technique Using an Enhanced Spatial Hash Join Algorithm," The KIPS Transactions:PartD, vol. 12, no. 2, pp. 179-188, 2005. DOI: 10.3745/KIPSTD.2005.12.2.179.

[ACM Style]
Young Bok Shim and Jong Yun Lee. 2005. Skewed Data Handling Technique Using an Enhanced Spatial Hash Join Algorithm. The KIPS Transactions:PartD, 12, 2, (2005), 179-188. DOI: 10.3745/KIPSTD.2005.12.2.179.