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Model-Driven Data Warehouse Automation: A Dependent-Concept Learning Approach

Model-Driven Data Warehouse Automation: A Dependent-Concept Learning Approach

Moez Essaidi, Aomar Osmani, Céline Rouveirol
Copyright: © 2017 |Pages: 29
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch113
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MLA

Essaidi, Moez, et al. "Model-Driven Data Warehouse Automation: A Dependent-Concept Learning Approach." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 2730-2758. https://doi.org/10.4018/978-1-5225-1759-7.ch113

APA

Essaidi, M., Osmani, A., & Rouveirol, C. (2017). Model-Driven Data Warehouse Automation: A Dependent-Concept Learning Approach. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 2730-2758). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch113

Chicago

Essaidi, Moez, Aomar Osmani, and Céline Rouveirol. "Model-Driven Data Warehouse Automation: A Dependent-Concept Learning Approach." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 2730-2758. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch113

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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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