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dc.contributor.authorEl Gebaly, Kareem
dc.date.accessioned2007-08-17 20:06:14 (GMT)
dc.date.available2007-08-17 20:06:14 (GMT)
dc.date.issued2007-08-17T20:06:14Z
dc.date.submitted2007
dc.identifier.urihttp://hdl.handle.net/10012/3163
dc.description.abstractAutomatic physical database design tools rely on ``what-if'' interfaces to the query optimizer to estimate the execution time of the training query workload under different candidate physical designs. The tools use these what-if interfaces to recommend physical designs that minimize the estimated execution time of the input training workload. Minimizing estimated execution time alone can lead to designs that are not robust to query optimizer errors and workload changes. In particular, if the optimizer makes errors in estimating the execution time of the workload queries, then the recommended physical design may actually degrade the performance of these queries. In this sense, the physical design is risky. Furthermore, if the production queries are slightly different from the training queries, the recommended physical design may not benefit them at all. In this sense, the physical design is not general. We define Risk and Generality as two new measures aimed at evaluating the robustness of a proposed physical database design, and we show how to extend the objective function being optimized by a generic physical design tool to take these measures into account. We have implemented a physical design advisor in PostqreSQL, and we use it to experimentally demonstrate the usefulness of our approach. We show that our two new metrics result in physical designs that are more robust, which means that the user can implement them with a higher degree of confidence. This is particularly important as we move towards truly zero-administration database systems in which there is not the possibility for a DBA to vet the recommendations of the physical design tool before applying them.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectDatabasesen
dc.subjectData Managementen
dc.subjectAutomatic Index Selectionen
dc.subjectPerformance Tuningen
dc.subjectSelf Managing Database Systemsen
dc.subjectRobustness in Database Physical Designen
dc.titleRobustness in Automatic Physical Database Designen
dc.typeMaster Thesisen
dc.pendingfalseen
dc.subject.programComputer Scienceen
uws-etd.degree.departmentSchool of Computer Scienceen
uws-etd.degreeMaster of Mathematicsen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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