Nicolas Bruno
Abstract
Existing solutions to the automated physical design problem in database systems attempt to minimize 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.
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