A Model Driven Architecture Approach to Generate Multidimensional Schemas of Data Warehouses

  • O. Betari MATSI Laboratory, Superior School of Technology Mohammed First University Oujda, Morocco
  • M. Erramdani MATSI Laboratory, Superior School of Technology Mohammed First University Oujda, Morocco
  • K. Arrhioui MISC Laboratory, Faculty of Sciences Ibn Tofail University Kenitra, Morocco
Keywords: Data Warehouse, Meta model, Model Driven Architecture, Transformation


Over the past decade, the concept of data warehousing has been widely accepted. The main reason for building data warehouses is to improve the quality of information in order to achieve specific business objectives such as competitive advantage or improved decision-making. However, there is no formal method for deriving a multidimensional schema from heterogeneous databases that is recognized as a standard by the OMG and the professionals of the field. Which is why, in this paper, we present a model-driven approach (MDA) for the design of data warehouses. To apply the MDA approach to the Data warehouse construction process, we describe a multidimensional meta-model and specify a set of transformations from a UML meta-model which is mapped to a multidimensional meta-model. The execution of the transformation, programmed by the Query View Transformation (QVT) language, takes as input an instance of the UML Meta-Model to generate an instance of the Dimensional Meta-Model as output.


(1) B. Vela, C. Blanco, E.F. Medina, E. Marcos, “A Practical Application of our MDD Approach for Modeling Secure XML Data Warehouses,” in Decision Support Systems (DSS), 54 (4), pp. 899-925, 2012.

(2) R. Kimball, M. Ross, The Data Warehouse Toolkit, 3rded., John Wiley & Sons, Inc. 2013.

(3) Object Management Group (OMG), MDA Guide 2.0.


(4) J.N. Mazón, J. Trujillo, “An MDA Approach for the Development of Data Warehouses,” in Decision Support Systems (DSS), 45 (1), pp. 41-58, 2008.

(5) D.S. Frankel, Model Driven Architecture: Applying MDA to Enterprise Computing, Wiley, 2003.

(6) A.G. Kleppe, J. Warmer, W. Bast, MDA Explained: The

Model Driven Architecture: Practice and Promise, Addison-Wesley, 2003.

(7) S. Mellor, K. Scott, A. Uhl, D. Weise, MDA Distilled: Principles of Model-driven Architecture,Addison-Wesley, 2004.

(8) M. Golfarelli, D. Maio, S. Rizzi, “The Dimensional Fact Model: a Conceptual Model for Data Warehouses,” in International Journal of Cooperative Information Systems (IJCIS), 7 (2&3), pp. 215-247, 1998.

(9) A. Abelló, J. Samos, F. Saltor, “YAM2: a Multidimensional Conceptual Model Extending UML,” in Information Systems (IS), 31 (6), pp. 541-567, 2006.

(10) J. Lechtenbörger, G. Vossen, “Multidimensional Normal Forms for Data Warehouse Design,” in Information Systems (IS), 25 (5), pp. 415-434, 2003.

(11) X. Blanc, MDA en Action: Ingénierie Logicielle Guidée par les Modèles, Eyrolles, 2005.

(12) S. Roubi, M. Erramdani, S. Mbarki, “Generating Graphical User Interfaces Based on Model Driven Engineering,” in International Review on Computers and Software (IRECOS), 10 (5), pp. 520-528, 2015.

(13) O.Betari, M. Erramdani, S. Roubi, K. Arrhioui, and S. Mbarki, “Model transformations in the MOF meta-modeling architecture: from UML to codeIgniter PHP framework,” Europe and MENA Cooperation Advances in Information and Communication Technologies, vol. 520, pp. 227-234, 2016.

(14) Object Management Group (OMG), MOF 2.0 QVT. http://www.omg.org/spec/MOF/2.0/PDF

(15) A. Vaisman, E. Zimányi, Data Warehouse Systems: Design and Implementation, Springer Berlin Heidelberg, 2014.

(16) W. H. Inmon, Building the Data Warehouse, John Wiley & Sons, 2002.

(17) E. Malinowski, E. Zimányi, Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Springer-Verlag Berlin Heidelberg, 2008.

(18) S.L. Mora, J. Trujillo, I.Y. Song, “A UML Profile for Multidimensional Modeling in Data Warehouses,” in Data & Knowledge Engineering, 59 (3), pp. 725-769, 2006.

(19) I.Arrassen, A.Meziane, R.Sbai, M.Erramdani, “QVT Transformation by Modelling - From UML Model to MD Model,” in International Journal of Advanced Computer Science and Applications (IJACSA), 2 (5), pp. 7-14, 2011.

(20) R. . S. Kaplan, D. P. Norton, “The Balanced Scorecard – Measures that Drive Performance,” in Harvard Business Review (HBR), 69 (1), pp. 71-79, 1992.

How to Cite
Betari, O., Erramdani, M., & Arrhioui, K. (2017). A Model Driven Architecture Approach to Generate Multidimensional Schemas of Data Warehouses. Transactions on Machine Learning and Artificial Intelligence, 5(4). https://doi.org/10.14738/tmlai.54.3197
Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems