Abstract
We present a case study about the application of the inductive database approach to the analysis of Web logs. We consider rich XML Web logs – called conceptual logs – that are generated by Web applications designed with the WebML conceptual model and developed with the WebRatio CASE tool. Conceptual logs integrate the usual information about user requests with meta-data concerning the structure of the content and the hypertext of a Web application. We apply a data mining language (MINE RULE) to conceptual logs in order to identify different types of patterns, such as: recurrent navigation paths, most frequently visited page contents, and anomalies (e.g., intrusion attempts or harmful usages of resources). We show that the exploitation of the nuggets of information embedded in the logs and of the specialized mining constructs provided by the query languages enables the rapid customization of the mining procedures following to the Web developers’ need. Given our on-field experience, we also suggest that the use of queries in advanced languages, as opposed to ad-hoc heuristics, eases the specification and the discovery of large spectrum of patterns.
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Meo, R., Lanzi, P.L., Matera, M., Esposito, R. (2006). Integrating Web Conceptual Modeling and Web Usage Mining. In: Mobasher, B., Nasraoui, O., Liu, B., Masand, B. (eds) Advances in Web Mining and Web Usage Analysis. WebKDD 2004. Lecture Notes in Computer Science(), vol 3932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11899402_9
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DOI: https://doi.org/10.1007/11899402_9
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