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Towards a Science of Database Systems

Towards a Science of Database Systems
迈向数据库系统科学
批准号:
0639106
负责人:
Richard Snodgrass
金额:
$0.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-09-01 至 2009-02-28

项目摘要

项目成果

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中文摘要
翻译
IIS-0639106 Richard T.亚利桑那大学数据库系统科学本研究采用了科学严谨的方法来研究一个以前由工程角度主导的领域,即数据库查询优化。 我们的目标是理解基于成本的查询优化器作为一个通用类的计算工件,发展洞察力和最终与预测理论如何这样的优化器behavior.The研究集中在一个方面,即基数估计。在为提交的查询确定最佳查询评估计划时,优化器估计每个候选计划的每个操作符产生的输出大小。已经观察到一个已建立的DBMS表现出“颤动”,其中查询评估计划来回跳跃,甚至来回交替,因为不可避免的基数估计不准确性增加。这个项目评估了颤振产生于优化器组件之间意外的相互作用的理论。 我们将在这一系列的DBMS中检查flutter的流行程度,看看这种流行程度(以表现出flutter的查询的百分比来衡量)是否与优化器复杂性的度量正相关。就更广泛的影响而言,这项研究研究了一种以前未知的基于成本的查询优化器现象,为明确考虑不确定性的查询优化新架构提供了科学的合理性。在这样做的过程中,它引入了一个新的视角(科学),增加了现有的数学和工程视角,现在突出地用于数据库研究,帮助实现赫伯特西蒙的愿景“人工科学”。“结果将通过项目网站http://www.cs.arizona.edu/soc/sodb传播。
英文摘要
IIS-0639106Richard T. Snodgrass rts@cs.arizona.eduUniversity of ArizonaTowards a Science of Database SystemsThis research takes a scientifically rigorous approach to an area previouslydominated by the engineering perspective, that of database queryoptimization. The goal is to understand cost-based query optimizers as ageneral class of computational artifacts, to develop insights and ultimatelywith predictive theories about how such optimizers behave.The research focuses on one aspect, that of cardinality estimation. Indetermining the optimal query evaluation plan for a submitted query, theoptimizer estimates the size of the output produced by each operator of eachcandidate plan. An established DBMS has been observed exhibiting "flutter,"in which the query evaluation plans jump around or even alternate back andforth as inevitable cardinality estimate inaccuracies increase. This projectevaluates the theory that the flutter arises out of unanticipatedinteractions between components of the optimizer. The prevalence of flutterwill be examined across this range of DBMSes, to see if that prevalence(measured as percentage of queries exhibiting flutter) is positivelycorrelated to measures of optimizer complexity.In terms of broader impacts, this research studies a previously unknownphenomenon of cost-based query optimizers, offering a scientific rationalefor new architectures for query optimization that explicitly take intoaccount uncertainty. In doing so, it introduces a new perspective (science),adding to the existing mathematical and engineering perspectives now usedprominently in database research, helping to realize Herbert Simon's visionof "sciences of the artificial." The results will be disseminated via theproject Web site http://www.cs.arizona.edu/soc/sodb.
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