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Cost effective storage space for data cubes

机译：具有成本效益的数据立方体存储空间

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摘要

View materialization is one of the most important techniques applied in multidimensional databases. The problem of selecting a set of views for materialization that minimizes queries response time under storage space constraint received significant attention over last twenty years. Many researchers concentrate on designing better view selection methods with respect to the running time or the cost of the solution. This paper summarizes our research on the problem of how much space should be allocated for views materialization to ensure good queries performance. In order to comprehensively investigate the problem and minimize the influence of untypical cases, the experiments described in this paper were done on the large data set, including large data cubes, rarely considered in previous papers. In particular, the relation between the number of data cube views and the space limit expressed as a percentage of the fully materialized data cube size and a multiple of the base view size is analysed. According to our experimental results, the allocation of large space for views materialization is not cost effective.
机译：视图实现是在多维数据库中应用的最重要的技术之一。在过去的二十年中，选择实现视图集以在存储空间限制下最大限度地缩短查询响应时间的问题受到了广泛关注。许多研究人员专注于针对运行时间或解决方案成本设计更好的视图选择方法。本文总结了我们关于应为视图实现分配多少空间以确保良好查询性能的问题的研究。为了全面调查问题并最大程度地减少非典型案例的影响，本文所述的实验是针对大型数据集（包括大型数据立方体）进行的，而以前的论文中很少考虑。尤其是，分析了数据多维数据集视图的数量与空间限制之间的关系，该关系表示为完全实现的数据多维数据集大小与基本视图大小的倍数的百分比。根据我们的实验结果，为视图物化分配大空间并不划算。

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