A Meta-model for Diverse Data Sources in Business Intelligence

Fatima Kalna; Abdessamad Belangour

doi:doi:10.11648/j.ajesa.20190701.11

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A Meta-model for Diverse Data Sources in Business Intelligence

Fatima Kalna, Abdessamad Belangour

Published in American Journal of Embedded Systems and Applications (Volume 7, Issue 1)

Received: 18 January 2019 Accepted: 28 February 2019 Published: 20 March 2019

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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.

Published in	American Journal of Embedded Systems and Applications (Volume 7, Issue 1)
DOI	10.11648/j.ajesa.20190701.11
Page(s)	1-8
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Business Intelligence, Data Source, Meta-Model, Relational, Multidimensional, NoSQL

References

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[9]	F. Atigui, Model-Driven Approach for Implementing and Reducing Data, 2013, pp 6-133.
[10]	R. Bruchez, Les bases de données NoSQL et le Big Data, Eyrolles 2nd edition 2015.
[11]	Object Management Group OMG, Common Warehouse Meta-model (CWM) Specification, Version1.1, March 2003.
[12]	X. Blanc, MDA in action: software engineering guided by models, Eyrolles, 2005.
[13]	F. Allilaire, T. Idrissi, Eclipse development tools for atl, http://www.sciences.univnantes.fr/lina/atl/Members/allilaire/Paper/ADT% AllilaireIdriss, 2004.
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APA Style

Fatima Kalna, Abdessamad Belangour. (2019). A Meta-model for Diverse Data Sources in Business Intelligence. American Journal of Embedded Systems and Applications, 7(1), 1-8. https://doi.org/10.11648/j.ajesa.20190701.11

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ACS Style

Fatima Kalna; Abdessamad Belangour. A Meta-model for Diverse Data Sources in Business Intelligence. Am. J. Embed. Syst. Appl. 2019, 7(1), 1-8. doi: 10.11648/j.ajesa.20190701.11

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AMA Style

Fatima Kalna, Abdessamad Belangour. A Meta-model for Diverse Data Sources in Business Intelligence. Am J Embed Syst Appl. 2019;7(1):1-8. doi: 10.11648/j.ajesa.20190701.11

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@article{10.11648/j.ajesa.20190701.11,
  author = {Fatima Kalna and Abdessamad Belangour},
  title = {A Meta-model for Diverse Data Sources in Business Intelligence},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {7},
  number = {1},
  pages = {1-8},
  doi = {10.11648/j.ajesa.20190701.11},
  url = {https://doi.org/10.11648/j.ajesa.20190701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20190701.11},
  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.},
 year = {2019}
}

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TY  - JOUR
T1  - A Meta-model for Diverse Data Sources in Business Intelligence
AU  - Fatima Kalna
AU  - Abdessamad Belangour
Y1  - 2019/03/20
PY  - 2019
N1  - https://doi.org/10.11648/j.ajesa.20190701.11
DO  - 10.11648/j.ajesa.20190701.11
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 1
EP  - 8
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20190701.11
AB  - 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.
VL  - 7
IS  - 1
ER  -

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Author Information

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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APA Style

Fatima Kalna, Abdessamad Belangour. (2019). A Meta-model for Diverse Data Sources in Business Intelligence. American Journal of Embedded Systems and Applications, 7(1), 1-8. https://doi.org/10.11648/j.ajesa.20190701.11

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ACS Style

Fatima Kalna; Abdessamad Belangour. A Meta-model for Diverse Data Sources in Business Intelligence. Am. J. Embed. Syst. Appl. 2019, 7(1), 1-8. doi: 10.11648/j.ajesa.20190701.11

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AMA Style

Fatima Kalna, Abdessamad Belangour. A Meta-model for Diverse Data Sources in Business Intelligence. Am J Embed Syst Appl. 2019;7(1):1-8. doi: 10.11648/j.ajesa.20190701.11

Copy | Download

@article{10.11648/j.ajesa.20190701.11,
  author = {Fatima Kalna and Abdessamad Belangour},
  title = {A Meta-model for Diverse Data Sources in Business Intelligence},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {7},
  number = {1},
  pages = {1-8},
  doi = {10.11648/j.ajesa.20190701.11},
  url = {https://doi.org/10.11648/j.ajesa.20190701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20190701.11},
  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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - A Meta-model for Diverse Data Sources in Business Intelligence
AU  - Fatima Kalna
AU  - Abdessamad Belangour
Y1  - 2019/03/20
PY  - 2019
N1  - https://doi.org/10.11648/j.ajesa.20190701.11
DO  - 10.11648/j.ajesa.20190701.11
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 1
EP  - 8
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20190701.11
AB  - 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.
VL  - 7
IS  - 1
ER  -

Copy | Download