Authors: Wilfried Grossmann, Marcus Hudec, Roland Kurzawa
Addresses: Department of Statistics and Decision Support Systems, University of Vienna, Universiatsstrasse 5/3, A-1010 Vienna, Austria. ' Department of Statistics and Decision Support Systems, University of Vienna, Universiatsstrasse 5/3, A-1010 Vienna, Austria. ' ec3 – Electronic Commerce Competence Center, Donau-City Strasse 1, A-1220 Vienna, Austria
Abstract: Clickstream data is one of the most important sources of information in websites usage and customers| behaviour in e-commerce applications. A number of web usage mining scenarios are possible depending on the available information. While simple traffic analysis based on clickstream data may easily be performed, such techniques are not adequate to substantially improve e-commerce activities. Meaningful results require a more complex research design. Questions of relevant data sources, possible applications and a prototype for web usage mining are briefly outlined in this paper. Aiming for more than simple traffic analysis, we need to develop models and tools to integrate additional databases such as customer data, content structure data and data from OLTP-systems. The design and implementation of such integrated data sources, which may be called data webhouse, is the necessary starting point of any analytical mining technique. The application is demonstrated within various examples.
Keywords: data warehousing; data mining; webhouse; web mining; clickstream data; e-commerce; electronic commerce; web usage; internet usage.
International Journal of Electronic Business, 2004 Vol.2 No.5, pp.480 - 492
Published online: 23 Dec 2004 *Full-text access for editors Access for subscribers Purchase this article Comment on this article