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Dominique Brodbeck
Researcher at University of Applied Sciences and Arts Northwestern Switzerland FHNW
Publications - 41
Citations - 2695
Dominique Brodbeck is an academic researcher from University of Applied Sciences and Arts Northwestern Switzerland FHNW. The author has contributed to research in topics: Visualization & Information visualization. The author has an hindex of 20, co-authored 41 publications receiving 2517 citations. Previous affiliations of Dominique Brodbeck include University of Basel & IBM.
Papers
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Book ChapterDOI
Persuasiveness of a mobile lifestyle coaching application using social facilitation
TL;DR: There was no significant difference between the two groups in terms of task compliance and health behaviour, and the effectiveness of mobility and social facilitation was confounded by other variables.
Journal ArticleDOI
A molecular dynamics investigation of rapid fracture mechanics
TL;DR: In this paper, the authors investigated dynamic fracture for two-dimensional notched solids under tension using million atom systems and found that the crack either follows a wavy path or branches and the anisotropy due to the large deformation at the crack tip plays the governing role in determining the crack path.
Journal ArticleDOI
The order of things: activity-centered information access
TL;DR: In this paper, the authors focus on the representation and access of Web-based information and how to make such a representation adapt to the activities or interests of individuals within a community of users.
Journal ArticleDOI
Visualization beyond the Desktop--the Next Big Thing
Jonathan C. Roberts,Panagiotis D. Ritsos,Sriram Karthik Badam,Dominique Brodbeck,Jessie Kennedy,Niklas Elmqvist +5 more
TL;DR: The next big thing is multisensory visualization that goes beyond the desktop, and visualization researchers need to develop and adapt to today's new devices and tomorrow's technology.
Proceedings Article
A Visual Approach for Monitoring Logs
Luc Girardin,Dominique Brodbeck +1 more
TL;DR: An approach to relieve system and network administrators from manually scanning sequences of log entries and using an experimental system based on unsupervised neural networks and spring layouts to automatically classify events contained in logs is described.