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

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.