Mining Web Logs for Actionable Knowledge

Everyday, popular Web sites attract millions of visitors. These visitors leave behind vast amount of Web site traversal information in the form of Web server and query logs. By analyzing these logs, it is possible to discover various kinds of knowledge, which can be applied to improve the performance of Web services. A particularly useful kind of knowledge is knowledge that can be immediately applied to the operation of the Web site; we call this type of knowledge the actionable knowledge. In this paper, we present three examples of actionable Web log mining. The first method is to mine a Web log for Markov models that can be used for improving caching and prefetching of Web objects. A second method is to use the mined knowledge for building better, adaptive user interfaces. The new user interface can adjust as the user behavior changes with time. Finally, we present an example of applying Web query log knowledge to improving Web search for a search engine application.