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Datawarehouse design for educational data mining

机译:教育数据挖掘的数据仓库设计

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Business intelligence (BI) builds upon a set of tools and applications that enable the analysis of vast amounts of information (Big Data). Educational institutions handle large volumes of Big Data every year. There is a strong need for the use of BI in these institutions to improve their processes and support decision making. The core technology in a BI project is a datawarehouse (DW). This paper describes the design considerations for the implementation of the DW in an educational scenario. The DW will be used in a knowledge discovery process to handle the information for the analysis of key performance indicators using educational data mining (EDM) techniques. The DW along with an enterprise architecture (EA) repository are the key technological assets of a knowledge management framework (KMF). This framework was designed to put order in the creation, capture, transfer and digitalization of knowledge. This guide and the framework are two of the outcomes of a research project in a private university. Furthermore, a case study suggests how to choose the best methodology in higher institutions. In the case study the steps for the DW design are presented. This study can be useful for academics and practitioners that plan to design a DW to analyze information using EDM techniques.
机译:Business Intelligence(BI)构建了一组工具和应用程序,可分析大量信息(大数据)。教育机构每年处理大量大数据。在这些机构中使用BI的需要,以改善他们的流程和支持决策。 BI项目中的核心技术是数据库(DW)。本文介绍了在教育场景中实现DW的设计考虑因素。 DW将用于知识发现过程,以处理使用教育数据挖掘(EDM)技术分析关键性能指标的信息。 DW以及企业架构(EA)存储库是知识管理框架(KMF)的关键技术资产。该框架旨在为知识创建,捕获,转移和数字化提供命令。本指南和该框架是私立大学研究项目的两项结果。此外,案例研究表明如何在高级机构中选择最佳方法。在研究中,提出了DW设计的步骤。该研究对于学院和从业者来说,该学术和从业者可以设计设计DW以使用EDM技术分析信息。

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