{"id":154426,"date":"2008-01-01T00:00:00","date_gmt":"2008-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/constrained-physical-design-tuning\/"},"modified":"2024-02-26T16:45:23","modified_gmt":"2024-02-27T00:45:23","slug":"constrained-physical-design-tuning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/constrained-physical-design-tuning\/","title":{"rendered":"Constrained Physical Design Tuning"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Existing solutions to the automated physical design problem in database systems attempt tominimize execution costs of input workloads for a given a storage constraint. In this paper, we argue that this model is not flexible enough to address several real-world situations. To overcome this limitation, we introduce a constraint language that is simple yet powerful enough to express many important scenarios. We build upon an existing transformation-based framework to effectively incorporate constraints in the search space. We then show experimentally that we are able to handle a rich class of constraints and that our proposed technique scales gracefully.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Existing solutions to the automated physical design problem in database systems attempt tominimize execution costs of input workloads for a given a storage constraint. In this paper, we argue that this model is not flexible enough to address several real-world situations. To overcome this limitation, we introduce a constraint language that is simple yet powerful 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