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Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons

Author

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  • Asma Dhaouadi

    (RIADI Laboratory, University of Manouba, Mannouba 2010, Tunisia
    LISTIC Laboratory, University of Savoie Mont Blanc, France Annecy-Chambéry, 74940 Chambéry, France
    Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis 1068, Tunisia)

  • Khadija Bousselmi

    (LISTIC Laboratory, University of Savoie Mont Blanc, France Annecy-Chambéry, 74940 Chambéry, France)

  • Mohamed Mohsen Gammoudi

    (RIADI Laboratory, University of Manouba, Mannouba 2010, Tunisia
    Higher Institute of Arts and Multimedia Manouba, University of Manouba, Manouba 2010, Tunisia)

  • Sébastien Monnet

    (LISTIC Laboratory, University of Savoie Mont Blanc, France Annecy-Chambéry, 74940 Chambéry, France)

  • Slimane Hammoudi

    (ERIS, ESEO-Grande Ecole d’Ingénieurs Généralistes, 49100 Angers, France)

Abstract

The extract, transform, and load (ETL) process is at the core of data warehousing architectures. As such, the success of data warehouse (DW) projects is essentially based on the proper modeling of the ETL process. As there is no standard model for the representation and design of this process, several researchers have made efforts to propose modeling methods based on different formalisms, such as unified modeling language (UML), ontology, model-driven architecture (MDA), model-driven development (MDD), and graphical flow, which includes business process model notation (BPMN), colored Petri nets (CPN), Yet Another Workflow Language (YAWL), CommonCube, entity modeling diagram (EMD), and so on. With the emergence of Big Data, despite the multitude of relevant approaches proposed for modeling the ETL process in classical environments, part of the community has been motivated to provide new data warehousing methods that support Big Data specifications. In this paper, we present a summary of relevant works related to the modeling of data warehousing approaches, from classical ETL processes to ELT design approaches. A systematic literature review is conducted and a detailed set of comparison criteria are defined in order to allow the reader to better understand the evolution of these processes. Our study paints a complete picture of ETL modeling approaches, from their advent to the era of Big Data, while comparing their main characteristics. This study allows for the identification of the main challenges and issues related to the design of Big Data warehousing systems, mainly involving the lack of a generic design model for data collection, storage, processing, querying, and analysis.

Suggested Citation

  • Asma Dhaouadi & Khadija Bousselmi & Mohamed Mohsen Gammoudi & Sébastien Monnet & Slimane Hammoudi, 2022. "Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons," Data, MDPI, vol. 7(8), pages 1-38, August.
  • Handle: RePEc:gam:jdataj:v:7:y:2022:i:8:p:113-:d:887021
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    References listed on IDEAS

    as
    1. Dimitrios Skoutas & Alkis Simitsis, 2007. "Ontology-Based Conceptual Design of ETL Processes for Both Structured and Semi-Structured Data," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 3(4), pages 1-24, October.
    2. Mahfoud Bala & Omar Boussaid & Zaia Alimazighi, 2016. "Extracting-Transforming-Loading Modeling Approach for Big Data Analytics," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 8(4), pages 50-69, October.
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