Welcome to the InfoSci Platform
Reference Hub1
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: © 2014 |Pages: 28
ISBN13: 9781466644946|ISBN10: 146664494X|EISBN13: 9781466644953
DOI: 10.4018/978-1-4666-4494-6.ch011
Cite Chapter Cite Chapter

MLA

Essaidi, Moez, et al. "Model-Driven Data Warehouse Automation: A Dependent-Concept Learning Approach." Advances and Applications in Model-Driven Engineering, edited by Vicente García Díaz, et al., IGI Global, 2014, pp. 240-267. https://doi.org/10.4018/978-1-4666-4494-6.ch011

APA

Essaidi, M., Osmani, A., & Rouveirol, C. (2014). Model-Driven Data Warehouse Automation: A Dependent-Concept Learning Approach. In V. Díaz, J. Lovelle, B. García-Bustelo, & O. Martinez (Eds.), Advances and Applications in Model-Driven Engineering (pp. 240-267). IGI Global. https://doi.org/10.4018/978-1-4666-4494-6.ch011

Chicago

Essaidi, Moez, Aomar Osmani, and Céline Rouveirol. "Model-Driven Data Warehouse Automation: A Dependent-Concept Learning Approach." In Advances and Applications in Model-Driven Engineering, edited by Vicente García Díaz, et al., 240-267. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4494-6.ch011

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.