B
Benoît Otjacques
Researcher at University of Lorraine
Publications - 76
Citations - 705
Benoît Otjacques is an academic researcher from University of Lorraine. The author has contributed to research in topics: Visualization & Information visualization. The author has an hindex of 11, co-authored 76 publications receiving 594 citations.
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Interoperability of E-Government Information Systems: Issues of Identification and Data Sharing
TL;DR: The results of an exploratory study in the government sector, focusing on the way public organizations manage identity-related data and the sharing of such data, either with other public agencies or with private organizations, draw conclusions about identity management in cross-border contexts.
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MINERVA-a platform for visualization and curation of molecular interaction networks.
Piotr Gawron,Marek Ostaszewski,Venkata P. Satagopam,Stephan Gebel,Alexander Mazein,Michael Kuzma,Simone Zorzan,Fintan McGee,Benoît Otjacques,Rudi Balling,Reinhard Schneider +10 more
TL;DR: The MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform is developed, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format that improves the efficiency of curation of networks.
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The State of the Art in Multilayer Network Visualization
TL;DR: In this article, the authors provide a state-of-the-art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multi-layer networks in the domain of complex systems, as well as those developing systems across application domains.
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Community extraction and visualization in social networks applied to Twitter
TL;DR: The proposed approach for social media analysis, especially for Twitter’s network, relies on a community identification based on a new community detection algorithm called Tribase and an interactive community visualization, which provides gradual knowledge acquisition using the authors' visualization tool, called NLCOMS.
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Cluster Sculptor, an interactive visual clustering system
TL;DR: Cluster Sculptor is a novel interactive clustering system that allows a user to iteratively update the cluster labels of a data set, and an associated low-dimensional projection, and uses a two-dimensional (2D) projection, both as support for overlaying the clusters, and engaging user interaction.