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A Meta-model for Diverse Data Sources in Business Intelligence
American Journal of Embedded Systems and Applications
Volume 7, Issue 1, June 2019, Pages: 1-8
Received: Jan. 18, 2019; Accepted: Feb. 28, 2019; Published: Mar. 20, 2019
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Authors
Fatima Kalna, Faculty of Science Ben M‘Sik, University Hassan II, Casablanca, Morocco
Abdessamad Belangour, Faculty of Science Ben M‘Sik, University Hassan II, Casablanca, Morocco
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Abstract
With the manifestation and evolution of Internet, several new types of data have emerged (videos, images, audio files, documents…). These new types of data classed as unstructured are more and more used and exchanged between IT systems, therefore their exploitation in Business Intelligence (BI) systems will absolutely provide a gold mine of information that guarantee a better and rich decision-making. Unfortunately BI systems don’t consider this sort of data and they are still limited to classical data sources: structured as Relational data source and semi-structured as XML files. Many research works separate the treatment and the design of a data warehouse that involves heterogeneous sources in order to avoid any problems of data integration and storage. However, the need for an approach that gathers diverse data sources still present. In this paper we appeal Model Driven Engineering (MDE) to propose a meta-model that assemble and describe all sort of structured, semi-structured and unstructured data sources such as relational, multidimensional, XML and NoSQL databases. Models conforming this meta-model will serve as an input for our BI process and for designing and modeling a data warehouse.
Keywords
Business Intelligence, Data Source, Meta-Model, Relational, Multidimensional, NoSQL
To cite this article
Fatima Kalna, Abdessamad Belangour, A Meta-model for Diverse Data Sources in Business Intelligence, American Journal of Embedded Systems and Applications. Vol. 7, No. 1, 2019, pp. 1-8. doi: 10.11648/j.ajesa.20190701.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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