Monitoring the Evolution of Web Usage Patterns

  • Steffan Baron
  • Myra Spiliopoulou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3209)


With the ongoing shift from off-line to on-line business processes, the Web has become an important business platform, and for most companies it is crucial to have an on-line presence which can be used to gather information about their products and/or services. However, in many cases there is a difference between the intended and the effective usage of a web site and, presently, many web site operators analyse the usage of their sites to improve their usability. But particularly in the context of the Internet, content and structure change rather quickly, and the way a web site is used may change often, either due to changing information needs of its visitors, or due to an evolving user group. Therefore, the discovered usage patterns need to be updated continuously to always reflect the actual behaviour of the visitors.

In this article, we introduce PAM, an automated Pattern Monitor, which can be used to observe changes to the behaviour of a web site’s visitors. It is based on a temporal representation of rules in which both the content of the rule and its statistical properties are modelled. It observes pattern change as evolution of the statistical measurements captured for a rule throughout its entire lifetime and notifies the user about interesting changes within the rule base. We present PAM in a case study on the evolution of web usage patterns. In particular, we discovered association rules from a web-server log that show which pages tend to be visited within the same user session. These patterns have been imported into the monitor, and their evolution throughout a period of 8 months has been analysed. Our results show that PAM is particularly suitable to gain insights into the changes of a rule base over time.


Change Detector Association Rule Training Phase Rule Base Domain Expert 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Steffan Baron
    • 1
  • Myra Spiliopoulou
    • 2
  1. 1.Institute of Information SystemsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Institute of Technical and Business Information SystemsOtto-von-Guericke-Universität MagdeburgMagdeburgGermany

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