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Creating a knowledge discovery model using MOEX's examination database for in-depth analysis and reporting

机译:使用MOEX的考试数据库创建知识发现模型以进行深入分析和报告

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Knowledge discovery related theories and technologies have been applied to all kind of databases recently in growing numbers due to their abilities in converting raw data into useful knowledge for operation management, decision making and in-depth analysis and reporting. The main purpose and objective of this study was to establish a knowledge discovery model using data warehouse technique to facilitate data gathering and in-depth analysis and news reporting. This study focused on the examination data collected by the Ministry of Examination (MOEX) in charge of Taiwan's certificate examinations as the material source used in this report. The main axis of the study was based on the literature of in-depth reporting applied to the MOEX Examinations data, especially those of Professional and Technical Personnel Examinations, combined with theories and related studies of Data Mining. One of the Data Mining techniques, the Associate Rule, was carried out to explore the MOEX Data Warehouse (MOEXDW) to verify the validity of the model for Knowledge Discovery in Database (KDD). This study arrived at two important findings 1). Changes in technical categories for Professional and Technical Personnel Examinations sponsored by MOEX were numerous and frequent as Taiwan's industries evolved from 1950 to 1991; 2). Technical Categories for Professional and Technical Personnel Examinations sponsored by MOEX remained unchanged from 1992 to present. Thus, this study prompted the following suggestions for MOEX: The Technical Categories for Professional and Technical Personnel Examination should be reviewed and adjusted to cope with the rapid evolutions of various industries in Taiwan. Furthermore, various education sectors should also properly review and adjust their respective curriculums to meet the industrial trends and requirements in their technical categories. These findings indicated the Knowledge Discovery in Data Warehouse can be a viable method in support of high quality in- depth analysis and significantly improve the quality and accuracies of a special in-depth report.
机译:由于与知识发现相关的理论和技术能够将原始数据转换为用于操作管理,决策以及深入分析和报告的有用知识的能力,因此近年来已越来越多地应用于各种数据库。这项研究的主要目的和目的是使用数据仓库技术建立一个知识发现模型,以促进数据收集以及深入的分析和新闻报道。这项研究的重点是由负责台湾证书考试的考试部(MOEX)收集的考试数据,作为本报告中使用的材料。研究的主轴是基于对MOEX考试数据(尤其是专业技术人员考试)进行深入报告的文献,并结合了数据挖掘的理论和相关研究。进行了一项数据挖掘技术,即关联规则,以探索MOEX数据仓库(MOEXDW),以验证用于数据库中知识发现(KDD)的模型的有效性。这项研究得出两个重要发现1)。随着台湾工业从1950年到1991年的发展,由MOEX赞助的专业和技术人员考试的技术类别变化非常频繁。 2)。由MOEX赞助的专业和技术人员考试的技术类别从1992年至今一直保持不变。因此,本研究为MOEX提出了以下建议:应审查和调整专业技术人员考试的技术类别,以适应台湾各个行业的快速发展。此外,各个教育部门还应适当审查和调整各自的课程,以适应其技术类别中的工业趋势和要求。这些发现表明,数据仓库中的知识发现可以成为支持高质量深度分析并显着提高特殊深度报告的质量和准确性的可行方法。

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