Research And Development Of Repertory Analysis System Based On Data Warehouse

Information, a most valuable resource in modern enterprises, is becoming increasingly important as the basis of scientific management, the premise of correct decision making and the effective means of regulation and control. Along with the progress of enterprise informationization, more and more data are stored in the enterprise database. It is an important and urgent issue for enterprise managers to consider how to effectively make use of these data, excavate valuable knowledge and rules to support enterprise management and decision making, and improve the enterprise comprehensive competitiveness.In recent years, decision support system (DSS) based on data warehouse has emerged along with the development of data warehouse (DW) technology, on-line analytical processing (OLAP) and data mining (DM) technology. Focusing on constructing the analysis and decision support system for the enterprise repertory, Firstly, there are introductions in this thesis that both domestic and international state-of-the-art development and application, concept and characteristics, structure, and modeling method of data warehouse; ETL (Extraction Transformation Loading) technique, OLAP, and data mining techniques, etc. Secondly, there is presentation the development of an analysis and decision support system for the enterprise repertory based on data warehouse. The viability and effectiveness of the system is validated using the historic data of Fuzhou West City Electronics Limited Company. Thirdly, the applications of a classical algorithm for mining association rules (Apriori algorithm), artificial neural network and decision tree algorithm, artificial neural networks in this system are discussed. Finally, with the comparison of the business enterprise ERP system in terms of structure, function, safety, etc., it is concluded that the data warehouse-based decision support system has superiorities. It also points out that there are some challenging problems still remaining in the system which need to be studied and improved.