D
Dominique Barth
Researcher at Versailles Saint-Quentin-en-Yvelines University
Publications - 121
Citations - 820
Dominique Barth is an academic researcher from Versailles Saint-Quentin-en-Yvelines University. The author has contributed to research in topics: Quality of service & Game theory. The author has an hindex of 13, co-authored 120 publications receiving 759 citations. Previous affiliations of Dominique Barth include Université Paris-Saclay & Centre national de la recherche scientifique.
Papers
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Journal ArticleDOI
Indexing in-network trajectory flows
TL;DR: This paper proposes T-PARINET, an access method to efficiently retrieve the trajectories of objects moving in networks, which significantly outperforms the reference R-tree-based access methods for in-network trajectory databases.
Journal ArticleDOI
On the b-continuity property of graphs
TL;DR: The b-chromatic numberb(G) of a graph G is the maximum number of colors for which G has a b-coloring, that is, proper colorings in which for each color c, there exists at least one vertex colored by c such that its neighbors are colored by each other color.
Journal ArticleDOI
A degree bound on decomposable trees
Dominique Barth,Hervé Fournier +1 more
TL;DR: It is shown that a decomposable tree has degree at most 4, and each degree-4 vertex of a decomPOSable tree is adjacent to a leaf, which leads to a polynomial time algorithm to decide if a multipode (a tree with only one vertex of degree greater than 2) is decomposition.
Journal ArticleDOI
Optimal dimensioning of the WDM unidirectional ECOFRAME optical packet ring
Bogdan Uscumlic,Isabella Cerutti,Annie Gravey,Philippe Gravey,Dominique Barth,Michel Morvan,Piero Castoldi +6 more
TL;DR: Results indicate that trading the wavelengths for receivers allows cost saving of up to 75% with respect to WDM optical packet rings with a single dedicated wavelength per node (i.e., single receiver at each node).
Proceedings ArticleDOI
Mobility Prediction Using Mobile User Profiles
TL;DR: This paper proposes an algorithmic approach to identify mobile-user profile which corresponds to frequent similar movements of a user in the neighbourhood graph of a cellular network and shows how to use them for an efficient mobility prediction.