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<p><it>Abstract</it>—This paper presents a methodology to the optimization in parallel join execution. The past researches on parallel join methods mostly focused on the design of algorithms for partitioning (e.g., hash) relations and distributing data buckets as evenly as possible to the processors. Once data are distributed to the processors, they assume that all processors will complete their tasks at about the same time. We stress that this is true if no further information such as page-level join index (to be discussed later) is available. Otherwise, the join execution can be further optimized and the workload in the processors may still be unbalanced. We study such problems that may incur in a shared-nothing architecture environment and propose algorithms to the problems in the paper. Also, a simulation study is performed to understand the characteristics of the proposed method.</p>
Bipartite graph, join index, parallel join execution, query optimization, relational database operations, workload balance.

C. Lee and Z. Chang, "Utilizing Page-Level Join Index for Optimization in Parallel Join Execution," in IEEE Transactions on Knowledge & Data Engineering, vol. 7, no. , pp. 900-914, 1995.
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