Parallelizing Query Optimization on Shared-Nothing Architectures
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The goal of multi-objective query optimization (MOQO) is to find query plans that realize a good compromise between conicting objectives such as minimizing execution time and minimizing monetary fees in a Cloud scenario. A previously proposed exhaustive MO ...
ACM Press2014
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Classical query optimization compares query plans according to one cost metric and associates each plan with a constant cost value. In this paper, we introduce the Multi-Objective Parametric Query Optimization (MPQ) problem where query plans are compared a ...
Query optimizers depend heavily on statistics representing column distributions to create efficient query plans. In many cases, though, statistics are outdated or non-existent, and the process of refreshing statistics is very expensive, especially for ad-h ...
Query plans offer diverse tradeoffs between conflicting cost metrics such as execution time, energy consumption, or execution fees in a multi-objective scenario. It is convenient for users to choose the desired cost tradeoff in an interactive process, dyna ...
Query optimizers depend heavily on statistics representing column distributions to create efficient query plans. In many cases, though, statistics are outdated or non-existent, and the process of refreshing statistics is very expensive, especially for ad-h ...
2014
,
The goal of multi-objective query optimization (MOQO) is to find query plans that realize a good compromise between conflicting objectives such as minimizing execution time and minimizing monetary fees in a Cloud scenario. A previously proposed exhaustive ...
Applications ranging from algorithmic trading to scientific data analysis require real-time analytics based on views over databases receiving thousands of updates each second. Such views have to be kept fresh at millisecond latencies. At the same time, the ...
