The Design And Implementation Of A Data Warehouse For Aiding Audit

With the popularity of the using of computers, most organizations in China have their accounting computerized. This innovation significantly has changed the auditing targets and raised the higher standards to the auditing. Currently, the auditing services however have not made related changes yet. Facing a large volume of data, the auditors could find difficulties in locating the problems in the data, and in summarizing the reusable experience. The establishment of data warehouse for aiding audit aims to resolve these problems.Firstly the dissertation introduces the overall design and system architecture of the data warehouse for aiding audit. The design plan and the functional architecture follow for the data preparation and data mining modules in Data Warehouse Manager. The designs and implementations of the data collection section and data pre-process section in data preparation module are also highlighted, as well as of the data mining model for aiding audit.The paper demonstrates the data mining algorithms used in the system by discussing the advantages and disadvantages of the current data clustering algorithms. Aimed to the disadvantages, K-CURE hierarchical clustering algorithm and LBCG grid density-based clustering algorithm are presented. The effectiveness of these algorithms is evaluated by the experimental results.The data warehouse for aiding audit outlined in this paper would contribute to the extraction, storage, transform and analysis to the auditing data. It would be able to help the auditors to point out the problems and the common points of these problems, and would be able to boost the evolution of the auditing methods. Meanwhile, the research and the improvement of the hierarchical clustering and density-based clustering algorithms to the massive volume of data would be useful references to the similar researches.