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Proposal of methodology for a data WareHousing process: Use case: Generation of indicators of academic productivity of a university)

机译:数据仓库过程的方法论:用例:大学学术生产力指标的产生)

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The article describes the methodology developed for a data warehousing process developed in a Chilean University. The purpose of the process is to generate indicators of academic productivity. For this a methodology is elaborated integrating diverse approaches techniques and methodologies such as: specification of information requirements relational modeling combined development model of Kimball and Hefesto proposals process of extraction-transformation-load (ETL) with a validation phase of indicators and integrated and interactive visualizations for the multidimensional analysis of the indicators with the use of dashboards. In this process three elements incorporated that in the opinion of the development team favored the success and effectiveness of the development of the solution. Firstly how to specify key performance indicators (KPI) in detail through the development of a template based on those used to specify information requirements in a software process. Second to explain the incorporation of a validation phase of the KPI obtained in the ETL process based on SQL (Structured Query Language) and the comparison with the data of the operational systems. Also in this ETL process is established as necessary and sufficient the use of a temporary repository as an area for data integration through a relational database. Thirdly integrated visualizations configured with an On-Line Analytical Processing (OLAP) tool which interactively display the related indicators for the same activity under analysis. Because of this data warehousing process you get a business intelligence platform based on a DataMart model with two schemes one star and another snowflake. The first contains the indicators of teacher productivity and the second those of scientific productivity which satisfy the specifications of the KPI and defined in agreement with the end users.
机译:本文介绍了为智利大学开发的数据仓库过程开发的方法。该过程的目的是生成学术生产力指标。为此,详细阐述了各种方法和方法的方法,例如:信息要求的规范和综合和互动可视化的验证阶段的Kimball和Hefesto提案过程中的信息需求关系建模组合模型。用于使用仪表板的指标的多维分析。在这一过程中,三个要素纳入了发展团队的意见,有利于解决方案发展的成功和有效性。首先,如何通过基于用于在软件过程中指定信息要求的那些,详细指定关键性能指标(KPI)。其次,为了基于SQL(结构化查询语言)和与操作系统的数据进行比较,解释在ETL过程中获得的KPI的验证阶段的验证阶段。同样在该ETL过程中,根据需要和充分使用临时存储库作为通过关系数据库的数据集成的区域建立。第三综合可视化配置,配置有一条在线分析处理(OLAP)工具,其交互式显示相关指标以在分析中进行相同的活动。由于此数据仓库过程,您可以基于DataMart模型获得一个商业智能平台,其中一个方案是一个星级和另一个雪花。第一个包含教师生产力的指标和四个科学生产力的指标,满足KPI规范,并与最终用户协议定义。

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