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Model-Driven Data Warehouse Automation: A Dependent-Concept Learning Approach
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Author(s): Moez Essaidi (Université Paris-Nord, France), Aomar Osmani (Université Paris-Nord, France)and Céline Rouveirol (Université Paris-Nord, France)
Copyright: 2014
Pages: 28
Source title:
Advances and Applications in Model-Driven Engineering
Source Author(s)/Editor(s): Vicente García Díaz (University of Oviedo, Spain), Juan Manuel Cueva Lovelle (University of Oviedo, Spain), B. Cristina Pelayo García-Bustelo (University of Oviedo, Spain)and Oscar Sanjuán Martinez (University of Carlos III, Spain)
DOI: 10.4018/978-1-4666-4494-6.ch011
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Abstract
Transformation design is a key step in model-driven engineering, and it is a very challenging task, particularly in context of the model-driven data warehouse. Currently, this process is ensured by human experts. The authors propose a new methodology using machine learning techniques to automatically derive these transformation rules. The main goal is to automatically derive the transformation rules to be applied in the model-driven data warehouse process. The proposed solution allows for a simple design of the decision support systems and the reduction of time and costs of development. The authors use the inductive logic programming framework to learn these transformation rules from examples of previous projects. Then, they find that in model-driven data warehouse application, dependencies exist between transformations. Therefore, the authors investigate a new machine learning methodology, learning dependent-concepts, that is suitable to solve this kind of problem. The experimental evaluation shows that the dependent-concept learning approach gives significantly better results.
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