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.
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Hernández, P., Garrigós, I., Mazón, JN. (2010). Model-Driven Development of Multidimensional Models from Web Log Files. In: Trujillo, J., et al. Advances in Conceptual Modeling – Applications and Challenges. ER 2010. Lecture Notes in Computer Science, vol 6413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16385-2_22
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DOI: https://doi.org/10.1007/978-3-642-16385-2_22
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