There is a number of OODB optimization techniques proposed recently, such as the translation of path expressions into joins and query unnesting, that can generate a large number of implicit joins even for simple queries. Unfortunately, most current commercial query optimizers are still based on the dynamic programming approach of Systems R, and cannot handle queries of more than ten tables. There is a number of recent proposals that advocate the use of combinatorial optimization techniques, such as iterative improvement and simulated annealing, to deal with the complexity of this problem. These techniques, though, fail to take advantage of the rich semantic information inherent in the query specification, such as the information available in query graphs, which gives a good handle to choose which relations to join each time.
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