Data Engineering team
The continuous increasing explosion of data on the Web, the large volumes of technical data, that come from sensors, available in many scientific fields, and the willpower of companies to publish their mass of data in order to share, integrate and exploit them in an efficient way to help in decisions making, led to a technical/method revolution related to the processing and use of data, commonly known as Data Science. Among the major challenges, we can mention the establishment of database management systems (DBMS) allowing the collection system, the integration, the persistence of data and models and the use of data in an efficient, flexible and smart way.
To take on these questions, our approach combines model-driven Engineering techniques, Knowledge and Database Engineering.
- Ontology based modeling,
- Data integration of heterogeneous sources,
- Big Data challenges,
- Performance and scalability,
- Green computing, personalization and usage,
- User-centered evaluation.
- Modeling of technical knowledge,
- Engineering of complex systems,
- Semantic Web,
- Data exchange and management, catalogues of technical components and exchange services,
- Patrimonial Data.
- Formal approaches for modeling and validation (Express, EMF/OCL),
- Languages for services and workflow description,
- Databases and data warehouses,
- Mathematical cost models,
- Simulation, software prototyping and validation.