Abstract
SpeedTracer, a World Wide Web usage mining and analysis tool, was developed to understand user surfing behavior by exploring the Web server log files with data mining techniques. As the popularity of the Web has exploded, there is a strong desire to understand user surfing behavior. However, it is difficult to perform user-oriented data mining and analysis directly on the server log files because they tend to be ambiguous and incomplete. With innovative algorithms, SpeedTracer first identifies user sessions by reconstructing user traversal paths. It does not require “cookies” or user registration for session identification. User privacy is protected. Once user sessions are identified, data mining algorithms are then applied to discover the most common traversal paths and groups of pages frequently visited together. Important user browsing patterns are manifested through the frequent traversal paths and page groups, helping the understanding of user surfing behavior. Three types of reports are prepared: user-based reports, path-based reports and group-based reports. In this paper, we describe the design of SpeedTracer and demonstrate some of its features with a few sample reports.
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