Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems

Data mining technology in the WMS system

Author MaShuang
Tutor ZengFanFeng;YuanMin
School North China University of
Course Computer technology
Keywords Data mining Web log mining Association Rule Apriori algorithm Personalization Recommendation
CLC TP311.13
Type Master's thesis
Year 2011
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By the increasing amount of information resources and user groups, the scale and the complexity of the Streaming Media on networks are also increasing. It is much more difficult to design and maintain the Stream Media websites, such as optimizing the Streaming Media servers’structures, offering high quality service to attract and retain more customers. Therefore, "Web Data Mining" is one of powerful tools to solve the requirements focusing on the research of the Streaming Media server system performance and service. Hence, it is to mine the useful information on Web by applying the thought and the method of Data Mining.Currently, the main techniques of Web log mining are association rule generating, sequence model discovering, classification, selecting and so on. Through analyzing the Web log, users’access interests, habits and the knowledge beneficial to website designing could be found. So that personalization service, market strategy and website self adjusting could also be produced. The data of web log mining include the access log of Web servers, the log of acting servers, the log of browsers, users’registration information, users’conversation or trade information and others. However, the major research is focus on the mining of log files presently.This article is based on the demands above, utilizing data mining techniques to investigate the WMS (Windows Media Service) Stream Media service log. After deeply analyzed the algorithms in data mining technique, the classical Apriori algorithm was applied in the association rule mining of the WMS Streaming Media service log. The details and the link relationships of users’access information were analyzed to form relevant models. By learning from users’behaviors and attributes, the interests and unique behavior patterns of each user could be acquired. Subsequently, it could provide theoretic basis for achieving Personalization Program Recommendation. Also, it could be conveniently realized that customizing distinct access UI. Finally, according to the acquired knowledge, the recommendation service to users could be timely provided.On the other hand, through applying the data mining results of the WMS log association rule to the video websites, the Personalization Recommendation System was designed and analyzed initially in this article. It is purpose to improve the management and maintenance of video websites, hence users could be easier to scan the merchandise information, who will be converted from scanners to purchasers then. In addition, it can raise the interselling abilities and the customs’loyalty to e-commerce websites.

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