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Название: Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs
Авторы: Zaiane O.R., Xin M., Han J.
As a confluence of data mining and WWW technologies, it is now possible to perform data mining on web log records collected from the Internet web page access history. The behaviour of the web page readers is imprinted in the web server log files. Analyzing and exploring regularities in this behaviour can improve system performance, enhance the quality and delivery of Internet information services to the end user, and identify population of potential customers for electronic commerce. Thus, by observing people using collections of data, data mining can bring considerable contribution to digital library designers.
In a joint effort between the TeleLearning-NCE project on Virtual University and NCE-IRIS project on data mining, we have been developing the knowledge discovery tool, WebLogMiner, for mining web server log files. This paper presents the design of the WebLog-Miner, reports the current progress, and outlines the future work in this direction.