Asma DHAOUADI

Doctoral student, LISTIC, USMB

Contact

E-mail: asma.dhaouadi@univ-smb.fr
Telephone: +33(0) 765228805

Office: A221-A222

Address 1: LISTIC - Polytech Annecy-Chambery, BP 80439, 74944 Annecy le Vieux Cedex, France

Thesis

Group: LISTIC - ReGaRD team
Theme: Modeling Data Warehousing in the context of Big Data
Subject: CONTRIBUTING TO MASSIVE DATA STORAGE: GENERIC ARCHITECTURE, METHODOLOGY AND IMPLEMENTATION
Summary:

In the era of Big Data, the rapid evolution of technologies makes the choice of tools adapted to experts' needs a complex one. Existing studies on Big Data storage and analysis are often limited and specific. To overcome these limitations, this thesis proposes a generic modeling approach, inspired by model-driven architecture, to guide technology choice at every stage. An interactive framework helps to model a customized pipeline and verify tool interoperability, with connector recommendations. This framework has been validated through case studies on COVID-19 and the 2022 World Cup. User feedback has been satisfactory (85% satisfaction rate).

Subject: CONTRIBUTION TO BIG DATA WAREHOUSING: GENERIC ARCHITECTURE, METHODOLOGY AND IMPLEMENTATION
ABSTRACT :
In the era of Big Data, the rapid evolution of tools creates a complex environment where experts struggle to select suitable solutions for specific needs. Existing studies on Big Data warehousing and analysis are often scenario-specific, limiting their applicability. This thesis proposes a top-down, model-driven architecture approach to provide a generic, flexible solution for modeling data warehousing and analytics, guiding experts in technology choices. We developed an interactive framework to help experts build customized data pipelines, ensuring interoperability of tools through a dedicated module. The framework was validated via case studies on COVID-19 and the 2022 FIFA World Cup, achieving an 85% user satisfaction rate.
Keywords: Data Warehouse, ETL Process Modeling, Data Warehousing Architectures, Knowledge Discovery, Meta-Model, Generic Methodology

Publications :
Dhaouadi, A., Bousselmi, K., Monnet, S., Gammoudi, M. M., & Hammoudi, S. (2024). A Machine Learning Based Decision Support Framework for Big Data Pipeline Modeling and Design. The Jordanian Journal of Computers and Information Technology (JJCIT), Vol. 10, No. 03, pp. 306 - 318. - https: //www.jjcit.org/paper/232/A-MACHINE-LEARNING-BASED-DECISION-SUPPORT-FRAMEWORK-FOR-BIG-DATA-PIPELINE-MODELING-AND-DESIGN
Dhaouadi, A., Paccoud W., Bousselmi, K., Monnet, S., Gammoudi, M. M. & Hammoudi, S. (2023). Big Data Tools: Interoperability Study and Performance Testing. IEEE Big Data 2023. https://ieeexplore. ieee.org/document/10386089>
Dhaouadi, A., Bousselmi, K., Gammoudi, M. M., Monnet, S., & Hammoudi, S. (2022). Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons. Data, 7(8), 113. — http s://doi.org/10.3390/data7080113
Dhaouadi, A., Bousselmi, K., Monnet, S., Gammoudi, M. M., & Hammoudi, S. (2022). A Multi-layer Modeling for the Generation of New Architectures for Big Data Warehousing. In International Conference on Advanced Information Networking and Applications (pp. 204-218). Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_18
Dhaouadi, A., Gammoudi, M. M., & Hammoudi, S. (2019). A Two Level Architecture for Data Warehousing and OLAP Over Big Data. Proceedings of the 34th International Business Information Management Association (IBIMA), ISBN: 978-0-9998551-3-3, 13-14 November 2019, Madrid, Spain, pp. 7182-7194. https://ibima.org/accepted-paper/a-two-level-architecture-for-data-warehousing-and-olap-over-big-data/

 

Supervisor: Sébastien Monnet & Mohamed Mohsen Gammoudi
Co-supervisor: Khadija Arfaoui
Start of thesis: January 2021