Warehousing and mining web logs

Abstract

Analyzing Web Logs for usage and access trends can not only provide important information to web site developers and administrators, but also help in creating adaptive web sites. While there are many existing tools that generate fixed reports from web logs, they typically do not allow ad-hoc analysis queries. Moreover, such tools cannot discover hidden patterns of access embedded in the access logs. We describe a relational OLAP (ROLAP) approach for creating a web-log warehouse. This is populated both from web logs, as well as the results of mining web logs. We also present a web based ad-hoc tool for analytic queries on the warehouse. We discuss the design criteria that influenced our choice of dimensions, facts and data granularity, and present the results from analyzing and mining the logs.