User:Gdaniel
From AtlanMod
Contents |
DANIEL Gwendal
PhD student in Computer Sciences at AtlanMod research team
I am actually working on persistence and query techniques for very large models. I am a committer of the NeoEMF project, and the developper of the Mogwaï query framework.
Recently I have been working on PrefetchML, a DSL and an execution engine for prefetching and caching models. The framework allows modellers to define prefetching/caching rules at the metamodel-level, regardless the backend used to store the model.
We have also reused our work on model-to-graph serialization in UMLtoGraphDB, a middleware generator that allows developpers to access a Graph database using informations defined at the conceptual model level.
Contact Informations
AtlanMod Team (Inria, Mines Nantes, LINA) - Room A242A
Ecole des Mines de Nantes
4, rue Alfred Kastler
44307 Nantes Cedex 3 - France
You can contact me at gwendal[dot]daniel[at]inria[dot]fr
Publications
- Gwendal Daniel, Gerson Sunyé, Amine Benelallam and Massimo Tisi. Improving memory efficiency for processing large-scale models. In BigMDE 2014, York, UK,September 2014
- Gwendal Daniel, Gerson Sunyé, and Jordi Cabot. Mogwaï: a Framework to Handle Complex Queries on Large Models. In RCIS 2016, Grenoble, FR,June 2016
- Gwendal Daniel, Gerson Sunyé, and Jordi Cabot. PrefetchML: a Framework for Prefetching and Caching Models. In MoDELS 2016, Saint-Malo, FR,October 2016 (Distinguished Paper Award)
- Gwendal Daniel, Gerson Sunyé, and Jordi Cabot. UMLtoGraphDB: Mapping Conceptual Schemas to Graph Databases. In ER 2016, Gifu, JP,November 2016
- Gwendal Daniel, Gerson Sunyé, Amine Benelallam, Massimo Tisi, Yoann Vernageau, Abel Gómez, and Jordi Cabot. NeoEMF: a Multi-database Model Persistence Framework for Very Large Models. In MoDELS 2016 (Tool Demo Track), Saint-Malo, FR,October 2016
Slides
- Mogwaï: a Framework to Handle Complex Queries on Large Models (RCIS 2016)
- NeoEMF: a Multi-database Model Persistence Framework for Very Large Models (MoDELS 2016 - Tool Demo Track)
- PrefetchML: a Framework for Prefetching and Caching Models (MoDELS 2016)
