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

Asma Dhaouadi (), Khadija Bousselmi, Mohamed Mohsen Gammoudi, Sébastien Monnet and Slimane Hammoudi
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Asma Dhaouadi: RIADI Laboratory, University of Manouba, Mannouba 2010, 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
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

Data, 2022, vol. 7, issue 8, 1-38

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.

Keywords: ETL process; data warehouse; ETL modeling; Big Data; UML; BPMN; ontology; MDA; graphical flow; systematic review (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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