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Identifying user sessions from web server logs with integer programming.

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Title: Identifying user sessions from web server logs with integer programming.
Authors: Román, Pablo E.1, Dell, Robert F.2, Velásquez, Juan D.3, Loyola, Pablo S.3
Source: Intelligent Data Analysis. 2014, Vol. 18 Issue 1, p43-61. 19p.
Subjects: Web analytics, Research on Internet users, Internet servers, Scholarly websites, Integer programming, Data mining
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Internet servers
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              Text: 2014
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