Identifying user sessions from web server logs with integer programming

Web usage mining has proven to be an important advance for e-business systems, both by finding web user buying patterns and suggesting ways to improve web user navigation. A primary input for web usage mining is web user sessions that must be constructed from web server logs (called sessionization) when such sessions are not otherwise identified. We use bipartite cardinality matching and a more general integer program to construct sessions. We also propose several variations of our integer program to provide additional insights into session characteristics. For testing, we retrieve 15 months of web server logs and corresponding real sessions from an academic web site. We compare real sessions, results obtained by our optimization models, and results from a commonly-used timeout heuristic. We find our optimization models dominate the timeout heuristic using several comparison measures. Solution time for a typical month is seven hours for our integer program, 30 minutes for our bipartite cardinality matching, and about 1 minute for the heuristic. Although solution time is significantly greater for the integer program, its variations contribute additional analysis of web user behavior.