Michel Hassenforder
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Michel Hassenforder has a total of 18 co-authors. Here are the top co-authors by number of shared publications:
- Laurent Thiry
Université de Haute Alsace
3 shared publications - Franck Fleurey
SINTEF ICT
1 shared publication - Frédéric Fondement
Université de Haute Alsace
1 shared publication - Germain Forestier
IRIMAS, University of Haute Alsace, Mulhouse, France
1 shared publication - H. Chanti1 shared publication
Heng Zhao
1 shared publication
Latest Publications
- 20 August 2023
- Published by Springer Nature
- p. 25-39
Enhancing GNN Feature Modeling for Document Information Extraction Using Transformers
- 20 August 2023
- Published by Springer Nature
- p. 25-39
Enhancing GNN Feature Modeling for Document Information Extraction Using Transformers
- Open Access
- 7 July 2018
- Published by Springer Nature in Journal of Big Data
- Vol. 5 (1) , 21
Categories for (Big) Data models and optimization
- 7 July 2018
- Published by Springer Nature in Journal of Big Data
- Vol. 5 (1) , 21
Open AccessCategories for (Big) Data models and optimization
- 1 July 2018
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 272-275
Categorical Models for BigData
This paper shows how concepts coming from category theory associated to a functional programming language can help to formalize and reason about data and get efficient programs in a BigData context. More precisely, it shows how data structures can be modeled by functors related by natural transformations (and isomorphisms). The transformation functions can then serve to shift a data structure and then get another program (eventually educing time complexity). The paper then explains the main concepts of the theory, how to apply them and gives an application to a concrete database and the performances obtained.
- 1 July 2018
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 272-275
Categorical Models for BigData
This paper shows how concepts coming from category theory associated to a functional programming language can help to formalize and reason about data and get efficient programs in a BigData context. More precisely, it shows how data structures can be modeled by functors related by natural transformations (and isomorphisms). The transformation functions can then serve to shift a data structure and then get another program (eventually educing time complexity). The paper then explains the main concepts of the theory, how to apply them and gives an application to a concrete database and the performances obtained.
- 25 February 2016
- Published by Lavoisier in Techniques et Sciences Informatiques
- Vol. 35 (1) , 31-53
Comment fusionner des ontologies avec la réécriture de graphes ?
- 25 February 2016
- Published by Lavoisier in Techniques et Sciences Informatiques
- Vol. 35 (1) , 31-53
Comment fusionner des ontologies avec la réécriture de graphes ?
- 1 January 2014
- Published by Elsevier in IFAC Proceedings Volumes
- Vol. 47 (3) , 1625-1630
Formalization and composition of languages for the modeling of fire safety systems
- 1 January 2014
- Published by Elsevier in IFAC Proceedings Volumes
- Vol. 47 (3) , 1625-1630
Formalization and composition of languages for the modeling of fire safety systems
- 23 April 2008
- Published by Springer Nature in Software and Systems Modeling
- Vol. 7 (4) , 423-441
Model-driven analysis and synthesis of textual concrete syntax
- 23 April 2008
- Published by Springer Nature in Software and Systems Modeling
- Vol. 7 (4) , 423-441
Model-driven analysis and synthesis of textual concrete syntax