Join strategies using data space partitioning
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In the recent investigations of reducing the relational join operation complexity several hash-based partitioned-join stategies have been introduced. All of these strategies depend upon the costly operation of data space partitioning before the join can be carried out. We had previously introduced a partitioned-join based on a dynamic and order preserving multidimensional data organization called DYOP. The present study extends the earlier research on DYOP and constructs a simulation model. The simulation studies on DYOP and subsequent comparisons of all the partitioned-join methodologies including DYOP have proven that space utilization of DYOP improves with the increasing number of attributes. Furthermore, the DYOP based join outperforms all the hash-based methodologies by greatly reducing the total I/O bandwidth required for the entire partitioned-join operation. The comparison model is independent of the architectural issues such as multiprocessing, multiple disk usage, and large memory availability all of which help to further increase the efficiency of the operation.
KeywordsJoin Projection Relational Databases/Algebra Database Machines Architectures Partitioned Joins Multi-dimensional Order Preserving Data Partitioning Performance Simulation
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