Model-driven development of multidimensional models from web log files

Authors:
Paul Hernández;Irene Garrigós;Jose-Norberto Mazón
Affiliations:
Lucentia Research Group, Dept. of Software and Computing Systems, University of Alicante, Spain;Lucentia Research Group, Dept. of Software and Computing Systems, University of Alicante, Spain;Lucentia Research Group, Dept. of Software and Computing Systems, University of Alicante, Spain
Venue:
ER'10 Proceedings of the 2010 international conference on Advances in conceptual modeling: applications and challenges
Year:
2010

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Abstract

Analyzing Web log data is important in order to study the usage of a website. Even though some approaches propose data warehousing techniques for structuring the Web log data into a multidimensional model, they present two main drawbacks: (i) they are based on informal guidelines and must be manually applied; and (ii) they consider data tailored to a specific Web log format, thus being restricted to specific analysis tools. To overcome these limitations, we present a model-driven approach for obtaining a conceptual multidimensional model from Web log data in a comprehensive, integrated and automatic manner. This approach consists of the following steps: (i) obtaining a conceptual model of the Web log data based on a unified metamodel, (ii) deriving a multidimensional model from this Web log model by formally defining a set of QVT (Query/View/Transformation) transformation rules.