Toward Multi-Approach Model for Semi-Automating a Data Warehousedesign from an Ontology

  • Morad Hajji Laboratory: Signals, Distributed Systems and Artificial Intelligence (SSDIA) ENSET Mohammedia, University Hassan II of Casablanca, Morocco
  • Mohammed Qbadou Laboratory: Signals, Distributed Systems and Artificial Intelligence (SSDIA) ENSET Mohammedia, University Hassan II of Casablanca, Morocco
  • Khalifa Mansouri Laboratory: Signals, Distributed Systems and Artificial Intelligence (SSDIA) ENSET Mohammedia, University Hassan II of Casablanca, Morocco
Keywords: Semantic Web, Business Intelligence, Data Warehouse, Ontology, Database, Coupling, Automatic, Design.

Abstract

The proliferation of projects that are part of the semantic Web is truly impressive. In fact, ontologies become increasingly present in information systems, they constitute great data sources that arouse the interest of being analyzed. Ontologies are used for standardizing, structuring and formalizing the Web, Web Service, E-learning systems, and other fields. Regarding multidimensional approaches, researches in this field have focused on direct Data Warehouse conception from an ontology, which do not integrate the intervention of the expert in this process. In this case, the transformations are global and not very customizable; it can reproduce the inherent defects from the data sources into the resulting data warehouse.

In this paper, we propose a new multi-approach model based on the coupling of relational database design approaches from an ontology with Data Warehouse design approaches from a relational database. Our model is semi-automatic allowing Data Warehouse design from an ontology by giving the designer more ability to intervene in this process and closely control the transformations. To assess the usefulness of our approach, we evaluated it by applying it on an example case study. The results of the example show that our approach is more accurate in terms of useful data filtering and adaptation of the multidimensional model to the end-users business-needs.

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Published
2017-09-01
Section
Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems