Asma DHAOUADI

Subject: CONTRIBUTING TO MASSIVE DATA STORAGE: GENERIC ARCHITECTURE, METHODOLOGY AND IMPLEMENTATION

Summary:

Data warehouses are indispensable for all information systems, as they play a key role in decision-making. The typical architecture of a Doctoral School consists mainly of four parts: data sources, data preparation, target data storage, and data access and analysis. At the heart of this architecture lies the ETL process for Extracting, Transforming and Loading data into the target database, for visualization, reporting, analysis and decision-making purposes. In the era of Big Data, the major challenge facing the community is to evolve traditional Doctoral School architectures, and in particular the classic ETL process, to support the requirements of . The state of the art reveals two limitations. The first concerns Big Data approaches based on various dedicated technologies, such as the Hadoop ecosystem, Flink, Kafka, Kibana and others. These are evolving rapidly, to the point where the architectures of Doctoral School are becoming obsolete compared with the latest technologies. The second is that there is no standard model for the representation and design of ETL processes. Despite the contributions of ETL process modeling work in the literature, the design of a generic ETL model capable of homogenizing the various contemporary approaches is still a topical issue. For these reasons, using model-driven engineering (MDE) as a generic framework and model-driven architecture (MDA) as a specific framework, we aim in this thesis to propose a new generic ETL model and a new generic architecture for massive data warehousing supporting this model. This architecture could be instantiated according to specific technologies depending on the application domain. In addition, we propose a methodology to help the expert implement an architecture that meets the specific needs of his company, based on the generic architecture. Finally, we validate all research work carried out on a practical case, such as the medical field (Pandemic covid-19) or other applications.