Nicolas Travers, Deputy Director of Research, and Ahmed Azough, Assistant Professor at ESILV, have recently co-authored a research article addressing the environmental implications of data modeling in cloud computing. Their study examines how the selection of data models can mitigate the carbon footprint associated with storing and processing information in digital infrastructures.
The research was conducted in the framework of the doctoral thesis of Jihane Mali, financed by ESILV, who is now an Assistant Professor at the school. Her work contributes to both academic research and teaching, reinforcing the integration of sustainability principles into ESILV’s engineering education.
Research Focus
The exponential growth of data in recent decades has created new challenges for Information Systems (IS) architects.
The increasing complexity of databases requires more sophisticated approaches to design, storage, and management, with a dual objective: ensuring performance while addressing cost and sustainability.
In this context, the ESILV research team developed DaMoOp, an automated methodology for guiding data model selection. Unlike most existing solutions, which primarily focus on transforming data models, DaMoOp provides a structured process for selecting the most suitable model based on multiple criteria, including environmental impact.
DaMoOp Methodology and Contributions
The DaMoOp framework begins with a conceptual model and an associated use case, which may include queries, parameters, and infrastructure constraints. From this foundation, it generates logical data models and applies a cost model to evaluate them.
This cost model is distinctive because it integrates not only financial and temporal dimensions but also environmental factors, such as energy consumption and related carbon emissions. The approach allows IS architects to:
- Compare data models through a multidimensional lens, balancing sustainability, cost, and efficiency.
- Adapt data model selection as use cases evolve.
- Use a simulation tool that visualizes the financial and ecological impact of different choices, thus supporting more informed decision-making.
By combining environmental, financial, and temporal perspectives, DaMoOp empowers organizations to pursue more sustainable practices in cloud computing, where energy consumption remains a pressing concern.
Integration in the ESILV Curriculum
Beyond the academic contribution, the methodology has been introduced in the DIA A5 – Big Data Structure course at ESILV.
Students learn to apply the DaMoOp process to assess how technical decisions in database design and management can influence the carbon footprint of digital systems.
This teaching approach reflects ESILV’s commitment to preparing engineers with both strong technical expertise and a clear understanding of the societal impact of digital technologies.
A Broader Research Strategy at ESILV
This publication is aligned with ESILV’s research policy, developed within the De Vinci Research Center. The school’s research strategy is built around four primary objectives:
- Scientific excellence, supported by a growing number of publications in leading academic journals and reinforced by efforts to disseminate knowledge through professional publications and outreach activities.
- Societal impact of research, with studies addressing challenges such as the ecological transition and digital responsibility.
- Interdisciplinarity, promoted through collaboration between management science and engineering researchers working in common laboratories.
- Internationalization, strengthened by partnerships with foreign universities and participation in European projects, which enhance the visibility and reach of ESILV’s research.
Looking Ahead
The work of Nicolas Travers, Ahmed Azough, and Jihane Mali illustrates how ESILV combines scientific rigor, societal relevance, and pedagogical integration in its research initiatives. By addressing the environmental footprint of cloud computing through innovative methodologies like DaMoOp, ESILV researchers contribute not only to academic debates but also to practical solutions for sustainable digital transformation.
Abstract of the Article
The complexity of database systems has increased in tandem with the exponential growth of data, necessitating Information Systems (IS) architects to continually refine data models and meticulously select storage and management options that align with requirements. While existing solutions focus on data model transformation, none offer guidance in selecting the most suitable model. In this context, we propose DaMoOp, an automated approach for leading data model selection process. DaMoOp starts from a conceptual model and associated use case comprising queries, settings, and infrastructure constraints, to generate relevant logical data models. A cost model, considering environmental, financial, and temporal factors, facilitates comparison and selection of the most suitable data model. Our cost model incorporates both data model and queries costs. Additionally, we suggest a data model selection process that enhances the ability to choose the optimal data model(s) for a specific use case, while also adapting to rapidly evolving use cases. We provide a strategic optimization approach designed to identify the most cost-efficient and stable data model as use case scenarios evolve. Moreover, we offer a simulation tool for the entire process, which enables visualizing the impact of use case variations on data model costs, thus empowering IS architects to make informed decisions.
Learn more about ESILV’s research strategy