A Fuzzy Data Warehouse Approach for Web Analytics

  • Daniel Fasel
  • Darius Zumstein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5736)


The analysis of web data and metrics became an important task of e-business to control and optimize the website, its usage and online marketing. Firstly, this paper shows the use of web analytics, different web metrics of Google Analytics and other Key Performance Indicators (KPIs) of e-business.

Secondly, this paper proposes a fuzzy data warehouse approach to improve web analytics. The fuzzy logic approach allows a more precise classification and segmentation of web metrics and the use of linguistic variables and terms. In addition, the fuzzy data warehouse model discusses the creation of fuzzy multidimensional classification spaces using dicing operations and shows the potential of fuzzy slices, dices and aggregations compared to sharp ones. The added value of web analytics, web usage mining and the fuzzy logic approach for the information and knowledge society are also discussed.


Fuzzy logic fuzzy classification data warehouse web analytics Google Analytics web usage mining web metrics electronic business 


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  1. 1.
  2. 2.
    Weischedel, B., Matear, S., Deans, K.: The Use of eMetrics in Strategic Marketing Decisions. Int. Journal of Internet Marketing and Advertising 2, 109–125 (2005)CrossRefGoogle Scholar
  3. 3.
    Sterne, J.: Web Metrics. Wiley, New York (2002)Google Scholar
  4. 4.
    Peterson, E.: Web Site Measurement Hacks. O’Reilly, New York (2005)Google Scholar
  5. 5.
    Kaushik, A.: Web Analytics – An Hour a Day. Wiley, New York (2007)Google Scholar
  6. 6.
    Clifton, B.: Advanced Web Metrics with Google Analytics. Wiley, New York (2008)Google Scholar
  7. 7.
    Hassler, M.: Web Analytics. MIT Press, Heidelberg (2008)Google Scholar
  8. 8.
  9. 9.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Zimmermann, H.-J.: Fuzzy Set Theory and its Applications. Kluwer, London (1992)Google Scholar
  11. 11.
    Werro, N.: Fuzzy Classification of Online Customers, Dissertation, University of Fribourg (2008),
  12. 12.
    Inmon, W.H., Strauss, D., Neushloss, G.: DW 2.0 – The Architecture for the Next Generation of Data Warehousing. Morgan Kaufmann, Burlington (2008)Google Scholar
  13. 13.
    Lehner, W.: Datenbanktechnologien für Data-Warehouse-Systeme – Konzepte und Methoden. dpunkt, Heidelberg (2003)Google Scholar
  14. 14.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit. Wiley, New York (2002)Google Scholar
  15. 15.
    Lenz, H.J., Shoshani, A.: Summarizability in OLAP and Statistical Data Bases. In: Proceedings of the Ninth International Conference on Scientific and Statistical Database Management, pp. 132–143. IEEE Computer Society, Washington (1997)Google Scholar
  16. 16.
    Schepperle, H., Merkel, A., Haag, A.: Erhalt von Imperfektion in einem Data Warehouse. In: Bauer, A., Böhnlein, M., Herden, O., Lehner, W. (eds.) Internationales Symposium: Data-Warehouse-Systeme und Knowledge-Discovery, pp. 33–42. Shaker, Aachen (2004)Google Scholar
  17. 17.
    Delgado, M., Molina, C., Sanchez, D., Vila, A., Rodriguez-Ariza, L.: A fuzzy multi-dimensional model for supporting imprecision in OLAP. In: IEEE International Conference on Fuzzy Systems, July 25-29, vol. 3, pp. 1331–1336 (2004)Google Scholar
  18. 18.
    Pérez, D., Somodevilla, M., Pineda, I.: Fuzzy Spatial Data Warehouse: A Multidimensional Model. In: Proceedings of the Eight Mexican International Conference on Current Trends in Computer Science, Morelia, September 24-28, 2007, pp. 3–9 (2007)Google Scholar
  19. 19.
    Alva, M.E., Martínez, A.B., Gayo, J.E.L., del Carmen Suárez, M., Cueva, J.M., Sagástegui, H.: Proposal of a Tool of Support to the Evaluation of User in Educative Web Sites. In: Lytras, M.D., Carroll, J.M., Damiani, E., Tennyson, R.D. (eds.) WSKS 2008. LNCS (LNAI), vol. 5288, pp. 149–157. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Liu, B.: Web Data Mining. Springer, New York (2007)zbMATHGoogle Scholar
  21. 21.
    Markow, Z., Larose, D.: Data Mining the Web. Wiley, New York (2007)CrossRefGoogle Scholar
  22. 22.
    Mobasher, B.: Web Usage Mining. In: Liu 2007, pp. 449–483 (2007)Google Scholar
  23. 23.
    Escobar-Jeria, V.H., Martín-Bautista, M.J., Sánchez, D., Vila, M.: Web Usage Mining Via Fuzzy Logic Techniques. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 243–252. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  24. 24.
    Berry, M., Linoff, G.: Mastering Data Mining. In: The Art and Science of Customer Relationship Management. Wiley, New York (2000)Google Scholar
  25. 25.
    Zumstein, D., Kaufmann, M.: A Fuzzy Web Analytics Model for Web Mining. In: Proc. of the IADIS European Conference on Data Mining, Algarve, Portugal, June 18-20 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Daniel Fasel
    • 1
  • Darius Zumstein
    • 1
  1. 1.Information Systems Research Group, Department of InformaticsUniversity of FribourgFribourgSwitzerland

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