Advertisement

A Framework for Web Usage Mining in Electronic Government

  • Ping Zhou
  • Zhongjian Le
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 252)

Abstract

Web usage mining has been a major component of management strategy to enhance organizational analysis and decision. The literature on Web usage mining that deals with strategies and technologies for effectively employing Web usage mining is quite vast. In recent years, E-government has received much attention from researchers and practitioners. Huge amounts of user access data are produced in Electronic government Web site everyday. The role of these data in the success of government management cannot be overstated because they affect government analysis, prediction, strategies, tactical, operational planning and control. Web usage miming in E-government has an important role to play in setting government objectives, discovering citizen behavior, and determining future courses of actions. Web usage mining in E-government has not received adequate attention from researchers or practitioners. We developed a framework to promote a better understanding of the importance of Web usage mining in E-government. Using the current literature, we developed the framework presented herein, in hopes that it would stimulate more interest in this important area.

Keywords

Association Rule Proxy Server Electronic Government Government Innovation Government Affair 
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.

References

  1. 1.
    Jiawei Han and Micheline kamber, Data Mining Concepts and Techniques (second edition)(China Machine Press, Bei Jing, 2006).MATHGoogle Scholar
  2. 2.
    Federico Michele Facca, Pier Luca Lanzi, Mining Interesting Knowledge from Weblogs: a survey, Data & Knowledge Engineering (53), 225–241 (2005).CrossRefGoogle Scholar
  3. 3.
    K.D. Fenstermacher, M. Ginsburg, Mining Client-side Activity for Personalization, in: Fourth IEEE International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS_02), 205–212 (2002).Google Scholar
  4. 4.
    Jaideep Srivastava, Robert Cooley, Mukund Deshpande et al, Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data [J]. SIGKDD Explorations, 1(2), 12–23 (2000).CrossRefGoogle Scholar
  5. 5.
    MOBASHER B, COOLEY R, SR IVASTAVA J, Creating Adaptive Web Sites Through Usage-based Clustering of URLs [C], Proceedings of the 1999 IEEE Knowledge and Data Engineering Exchange Workshop (KDEXp99), 19–25 (1999).Google Scholar
  6. 6.
    LIU JG, WU W P. Web Usage Mining for Electronic Business Applications [J], Machine Learning and Cybernetics (2), 1314–1318 (2004).Google Scholar
  7. 7.
    Mobasher B, Cooly R. Srivastara J. Automatic Personalization Based on Web Usage Mining [J], Communications of the ACM, 43(8), 142–151 (2000).CrossRefGoogle Scholar
  8. 8.
    Przemysław Kazienko, Michał Adamski., AdROSA—Adaptive Personalization of Web Advertising. Information Sciences (177), 2269–2295 (2007).CrossRefGoogle Scholar
  9. 9.
    Elisabeth N. Bui, Brent L. Henderson and Karin Viergever, Knowledge Discovery from Models of Soil Properties Developed Through Data Mining, Ecological Modeling (5), 431–446 (2006).CrossRefGoogle Scholar
  10. 10.
    Milakovich, M. and Gordon, G., Public Administration in America (7th Ed.), Bedford/St. Martin’s (New York, 2001).Google Scholar
  11. 11.
    C.R. Anderson, A Machine Learning Approach to Web Personalization, Ph.D. thesis, University of Washington (2002).Google Scholar
  12. 12.
    B. Diebold, M. Kaufmann, Usage-based Visualization of Web Localities, in: Australian symposium on information visualization, 159–164 (2001).Google Scholar
  13. 13.
    Paul Beynon-Davies, Constructing Electronic Government: the case of the UK inland revenue, International Journal of Information Management (25), 3–20 (2005).Google Scholar
  14. 14.
    Haibin Liu, Vlado Keselj, Combined Mining of Web Server Logs and Web Contents for Classifying User Navigation Patterns and Predicting Users’ Future Requests, Data & Knowledge Engineering (61), 304–330 (2007).CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Ping Zhou
    • 1
  • Zhongjian Le
    • 1
  1. 1.School of Information ManagementJiangXi University of Finance and EconomicNanChangChina

Personalised recommendations