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Expediting analytical databases with columnar approach

机译:使用列式方法加速分析数据库

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The approaches and discussions given in this paper offer applicable solutions for a number of scenarios taking place in the contemporary world that are dealing with performance issues in development and use of analytical databases for the support of both tactical and strategic decision making. The paper introduces a novel method for expediting the development and use of analytical databases that combines columnar database technology with an approach based on denormalizing data tables for analysis and decision support. This method improves the feasibility and quality of tactical decision making by making critical information more readily available. It also improves the quality of longer term strategic decision making by widening the range of feasible queries against the vast amounts of available information. The advantages include the improvements in the performance of the ETL process (the most common time-consuming bottleneck in most implementations of data warehousing for quality decision support) and in the performance of the individual analytical queries. These improvements in the critical decision support infrastructure are achieved without resulting in insurmountable storage-size increase requirements. The efficiencies and advantages of the introduced approach are illustrated by showing the application in two relevant real-world cases.(C) 2017 Elsevier B.V. All rights reserved.
机译:本文给出的方法和讨论为当今世界发生的许多场景提供了适用的解决方案,这些场景正在处理开发和使用分析数据库以支持战术和战略决策的性能问题。本文介绍了一种加快分析数据库开发和使用速度的新方法,该方法将列式数据库技术与一种基于非规范化数据表的方法相结合的方法用于分析和决策支持。通过使关键信息更容易获得,此方法提高了战术决策的可行性和质量。通过扩大针对大量可用信息的可行查询的范围,它还提高了长期战略决策的质量。优点包括改进了ETL流程的性能(在大多数用于质量决策支持的数据仓库实现中最常见的耗时瓶颈)和单个分析查询的性能。在关键决策支持基础架构中实现了这些改进,而又不会导致无法克服的存储大小增长需求。通过在两个相关的实际案例中展示该应用程序,可以说明引入的方法的效率和优势。(C)2017 Elsevier B.V.保留所有权利。

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