An R*-tree Based Semi-Dynamic Clustering Method for the Efficient Processing of Spatial Join in a Shared-Nothing Parallel Database System
Kevin Shaw (Stennis Space Center)John Sample (Stennis Space Center)
Mahdi Abdelguerfi (University of New Orleans, LA)
Gayatri Ganpaa (University of New Orleans, LA)
Maik Flanagin (University of New Orleans, LA)
Abstract:
The spatial join is a computationally expensive operator to implement. The efficient implementation of the spatial join operator is, thus, desirable. This paper discusses a new parallel algorithm that implements the spatial join in an efficient manner. The proposed algorithm is compared to an existing parallel spatial join algorithm, the clone join. Both algorithms have been implemented on a Beowulf cluster and compared using real datasets. An extensive experimental analysis reveals that the proposed algorithm exhibits superior performance both in declustering time as well as in the execution time of the spatial Join query.
Keywords:
Cluster and Grid Computing, Data Processing, Parallel and Distributed Computing,
