American Journal of Embedded Systems and Applications

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A Meta-model for Diverse Data Sources in Business Intelligence

Received: Jan. 18, 2019    Accepted: Feb. 28, 2019    Published: Mar. 20, 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.

DOI 10.11648/j.ajesa.20190701.11
Published in American Journal of Embedded Systems and Applications ( Volume 7, Issue 1, June 2019 )
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), 2024. Published by Science Publishing Group

Keywords

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

References
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[3] T. Etienne, Establishment of a data warehouse for the strategic decision, Academia, 2008, pp 2.
[4] R. Kimball, The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouse, John Wiley, 1996.
[5] B. Inmon, Building the Data Warehouse, John Wiley and Sons, New York, 1996.
[6] W. Brand, NoSQL For Dummies, John Wiley & Sons, 2015.
[7] F. Abdelhadi, A. Ait Brahim, F. Atigui, G. Zurfluh, Logical Unified Modeling For NOSQL Databases, ICEIS, 2017.
[8] J. Han, E. Haihong, G. Le, J. Du, Survey on NoSQL Database, Pervasive Computing and Applications, ICPCA 6th International Conference, 2011.
[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.
[14] P. Vassiliadis, A Survey of Extract-Transform-Load Technology, International Journal of Data Warehousing and Mining IJDWM, 2009.
[15] Object Management Group OMG, Meta Object Facility (MOF) Core Specification, Version 2.4.1, August 2011.
[16] C. Favre, F. Bentayeb, O. Boussaid, J. Darmont, G. Gavin, N. Harbi, N. Kabachi, S. Loudcher, Data warehouses for dummies. . . or not !, 2nd Workshop helps the Decision at all Floors (EGC / AIDE) , January 2013.
[17] F. Ravat, O. Teste, R. Tournier, G. Zuruh, Algebraic and graphic languages for olap manipulations, International Journal of Data Warehousing and Mining IJDWM, 2008, pp.17-46.
[18] A. Abello, Big data design, DOLAP, 2015.
[19] K. Dehbouh, F. Bentayed, O. Boussaid, N. Kabachi, Using the column oriented model for implemeting big data warehouses, PDPTA, 2015.
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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://download.sciencepg.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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    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.
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Author Information
  • Faculty of Science Ben M‘Sik, University Hassan II, Casablanca, Morocco

  • Faculty of Science Ben M‘Sik, University Hassan II, Casablanca, Morocco

  • Section