1994
DOI: 10.1145/191843.191872
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Optimization of dynamic query evaluation plans

Abstract: Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and R… Show more

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Cited by 52 publications
(52 citation statements)
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“…Optimizing a query into a single plan may result in a substantially sub-optimal plan if the actual values are different from those assumed at optimization time [GW89]. To overcome this problem, parametric query optimization (PQO) optimizes a query into a number of candidate plans, each optimal for some region of the parameter space [CG94,INSS97,INSS92,GK94,Gan98]. At run time, when the actual parameter values are known, the appropriate plan can be chosen.…”
Section: Permission To Copy Without Fee All or Part Of This Materials mentioning
confidence: 99%
“…Optimizing a query into a single plan may result in a substantially sub-optimal plan if the actual values are different from those assumed at optimization time [GW89]. To overcome this problem, parametric query optimization (PQO) optimizes a query into a number of candidate plans, each optimal for some region of the parameter space [CG94,INSS97,INSS92,GK94,Gan98]. At run time, when the actual parameter values are known, the appropriate plan can be chosen.…”
Section: Permission To Copy Without Fee All or Part Of This Materials mentioning
confidence: 99%
“…The use of intervals may seem similar to the principle of parametric optimization [10,15] where different plans are generated for different intervals of the optimization parameters. The main limitation of this approach is the large number of plans to generate, store and compare at the optimization phase.…”
Section: Related Workmentioning
confidence: 99%
“…Graefe and Ward [15] focused on determining when re-optimizing a given query that is issued repeatedly is necessary. Subsequently, Colde and Graefe [8] proposed a new query optimization model which constructs dynamic plans at compile-time and delays some of the query optimization until run-time. Kabra and DeWitt [21] proposed an approach collecting statistics during the execution of complex queries in order to dynamically correct suboptimal query execution plans.…”
Section: Related Workmentioning
confidence: 99%