Data Warehouse And Data Mining Used In The Logistics System And Its Research

With the computer information technology and economic development, all aspects of the logistics industry which demand on business management have became higher and higher, and the information processing is also a growing need an advanced information technology to support. Data warehouse and data mining methods can improve all aspects of logistics efficiency and avoid unnecessary waste, and it also can use the past scattered information. Data Warehouse and Data Mining methods can establish the logistics decision mechanism, and reduce the operating costs and product costs, so it can accelerate cash flow. In this paper, we discussed that how to use the data warehouse and data mining to combined, and used it in logistics management areas. In this way, it can explore a new way of thinking for the logistics industry.This thesis researched the theory and technology of data warehouse, and discussed that how to use the data warehouse technology in logistics management. In this paper, it detailed analysis the business activities and data of the logistics management. And we designed the data warehouse data structure and logic model, and the concept of data warehouse model, logic model, logic model, the generation of data warehouse, data warehouse usage and maintenance, and ultimately to determine the theme of the system: marketing profile the volume of goods into and out of warehouses, customer credit analysis, commodity distribution arrival rate of quasi-point analysis, accounts receivable analysis. Described in detail the design and realization of the process, through the SQL Server 2005's SSIS components, to achieve a data source for data extraction, transformation and loading, and use SSIS to establish a data cube in order to meet the needs of OLAP.In addition, we have researched the association rules and data classification algorithms, and used this method in the system. Through the excavation of which the law contains, it can provide the marketing strategies and the credit of potential of high quality customers to logistics management. Enterprises to reasonable adjustment of the rational management of resources, improve operational efficiency, reduce costs and achieve better business and consumer market by the interaction between the effective and timely completion of the logistics operation; the analysis of customer credit applications, to facilitate the enterprises should be collection management, and reduce losses, reduce bad debt losses, for the low credit rating of customers, it can enhance awareness in the future to reduce losses.Because of the data, data mining algorithm for mining tasks and the diversity of study leading to a lot of problems, finally, the thesis introduced the development trend of data mining.