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<p>Most algorithms for determining query processing strategies in distributed databases are static in nature; that is, the strategy is completely determined on the basis of a priori estimates of the size of intermediate results, and it remains unchanged throughout its execution. The static approach may be far from optimal because it denies the opportunity to reschedule operations if size estimates are found to be inaccurate. Adaptive query execution may be used to alleviate this problem. A low overhead delay method is proposed to decide when to correct a strategy. Sampling is used to estimate the size of relations, and alternative heuristic strategies prepared in a background mode are used to decide when to correct. Evaluation using a model of a distributed database indicates that the heuristic strategies are near optimal. Moreover, it also suggests that it is usually correct to abort creation of an intermediate relation which is much larger than predicted.</p>
adaptive query execution; sampling; distributed query processing; distributed databases; a priori estimates; static approach; low overhead delay; heuristic strategies; distributed databases; heuristic programming; information retrieval

J. Riordan, J. Pyra and P. Bodorik, "Deciding to Correct Distributed Query Processing," in IEEE Transactions on Knowledge & Data Engineering, vol. 4, no. , pp. 253-265, 1992.
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