| Summary: | 碩士 === 國立臺北教育大學 === 資訊科學系碩士班 === 104 === This research uses professional domain knowledge proposed by auditors to transform domain knowledge into a rule base of audit knowledge through programs, forming many control points to assist auditors to find high-risk information in Big Data. With collection mapping methods, the rule base of audit knowledge is mapped into sets needed for data quality analysis, and collective attributive analysis is utilized to analyze the data quality of informative archives which were sent by Government Institutions. Considering the actual need and execution effectiveness of audit affairs, this data quality analysis only records abnormal data to reduce storage space and promotes implementation effectiveness.
The research method can analyze informative archives on time before institutions send them. The results indicate that the abnormal informative archives and influential control points can allow auditors to realize the data quality of informative archives in order to avoid auditing abnormal data and to reduce misinterpreting analyzed results. In response to the increasing data which were send by institutions, this research proposed a plan and design of database to promote the implementation effectiveness of the database system.
This research is integrated in the audit analysis system after being tested and is applied to audit works in auditing informative archives, which proved the effectiveness and availability of this research method.
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