Efficient mining and prediction of user behavior patterns in mobile web systems

S. Tseng*, Kawuu W. Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Scopus citations


The development of wireless and web technologies has allowed the mobile users to request various kinds of services by mobile devices at anytime and anywhere. Helping the users obtain needed information effectively is an important issue in the mobile web systems. Discovery of user behavior can highly benefit the enhancements on system performance and quality of services. Obviously, the mobile user's behavior patterns, in which the location and the service are inherently coexistent, become more complex than those of the traditional web systems. In this paper, we propose a novel data mining method, namely SMAP-Mine that can efficiently discover mobile users' sequential movement patterns associated with requested services. Moreover, the corresponding prediction strategies are also proposed. Through empirical evaluation under various simulation conditions, SMAP-Mine is shown to deliver excellent performance in terms of accuracy, execution efficiency and scalability. Meanwhile, the proposed prediction strategies are also verified to be effective in measurements of precision, hit ratio and applicability.

Original languageEnglish
Pages (from-to)357-369
Number of pages13
JournalInformation and Software Technology
Issue number6
StatePublished - 1 Jun 2006


  • Data mining
  • Location prediction
  • Location-based services
  • Mobile web system
  • Mobility prediction

Fingerprint Dive into the research topics of 'Efficient mining and prediction of user behavior patterns in mobile web systems'. Together they form a unique fingerprint.

Cite this