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<p>Optimizing large join queries that consist of many joins has been recognized as NP-hard. Most of the previous work focuses on a uniprocessor environment. In a multiprocessor, the location of each join adds another dimension to the complexity of the problem. In this paper, we examine the feasibility of exploiting the inherent parallelism in optimizing large join queries on a hypercube multiprocessor. This includes using the multiprocessor not only to answer the large join query but also to optimize it. We propose an algorithm to estimate the cost of a parallel large join plan. Three heuristics are provided for generating an initial solution, which is further optimized by an iterative local-improvement method. The entire process of parallel query optimization and execution is simulated on an Intel iPSC/2 hypercube machine. Our experimental results show that the performance of each heuristic depends on the characteristics of the query.</p>
optimisation; computational complexity; simulated annealing; iterative methods; heuristic programming; relational algebra; hypercube networks; query processing; parallel programming; large join optimization; hypercube multiprocessor; large join queries; NP-hard problem; problem complexity; inherent parallelism; parallel large join plan; heuristics; initial solution; iterative local-improvement method; Intel iPSC/2 hypercube machine; performance; relational database

E. Omiecinski, E. Lin and S. Yalamanchili, "Large Join Optimization on a Hypercube Multiprocessor," in IEEE Transactions on Knowledge & Data Engineering, vol. 6, no. , pp. 304-315, 1994.
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