Web Usage Mining: Information and Pattern Discovery on the World Wide Web 
Using Web Server Logs

Amitha Caldera

Abstract
 
Web Mining is the extraction of interesting and potentially useful patterns from World 
Wide Web (WWW). Web Mining can be categorized into three areas of interest based 
on which part of the Web to mine: Web Content Mining, Web Structure Mining and 
Web Usage Mining. Web Content Mining is the discovery of useful information from 
the Web contents or documents. Web Structure Mining is the process of inferring 
knowledge from the hyperlink structure of the Web. Web Usage Mining is the 
discovery of interesting user access patterns from Web server logs.

With the rapid progress of WWW technology, and the ever growing popularity of the 
WWW, the WWW continues to expand with over one billion connected users 
worldwide and over 800 million pages on many different subjects. Popular Web sites 
can see their Web server logs growing by hundreds of megabytes everyday. The 
major challenge involved in Web Usage Mining is pre-processing of raw server log 
data in the presence of caching and proxy servers to provide an accurate picture of 
how a site is being used. The results of the Web Usage Mining have become critical 
for number of applications such as Web site modification, system design 
improvement, server performance enhancement, business and marketing decision 
support.

In my presentation, current practices of data pre-processing methods and their 
shortcomings, and the applications of data mining techniques to Web server logs in 
order to discover interesting access patterns will be discussed.