Design And Realization Of Freight Agency Enterprise Management Information System

This paper completes the design and realization of freight agency enterprise management information system and data mining is introduced into the enterprise's management. In communication,the browse information of users are collected automatically by web server. The web logs are analyzed effectively and the ralationship between pages and users is expatiated,which could optimize the system's structure and supply references for the enterprise to make market business strategy in order to help enterprise gain much more advantage of competition.This paper realizes Management Information System of some freight agency enterprise with ASP(Active Server Page) technology based on B/S mode. According to the practical situation of enterprise, we analyze,design, and realize the MIS based on Web. The main technology is analyzed such as ASP and ADO(ActiveX Data Objects) in the process of developing web database. As for the part of data inquiry,the paper realizes fuzzy inquiry and long sentence inquiry which make inquiry more convenient and flexible.The application of Web usage mining in the enterprise's management is described in this paper. Based on the latest researched results, the classification, process and scope of web usage mining are described detailedly and the method and technology of web usage mining are also explained.The web usage mining is based on internet. Data in internet is half structure and it is difficult to deal with.Fortunately, the web server log files have a nice structure and it is convenient for data mining.The relevant matrix clustering algorithm is used for clustering users and pages.If accessing frequency of web is considered, the accessing situation of website will be reflected more exactly.The clustering results reflects similar users and correlative pages, which provides references for web manager improving web pages. It offers possibility for applying personal service with electronic business site.At the end,the future direction and works in web log mining were proposed.