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webVis/instant3DHub: Visual computing as a service infrastructure to deliver adaptive, secure and scalable user centric data visualisation

: Behr, Johannes; Mouton, Christophe; Parfouru, Samuel; Champeau, Julien; Jeulin, Clotilde; Thöner, Maik; Stein, Christian; Schmitt, Michael; Limper, Max; Sousa, Miguel de; Franke, Tobias; Voss, Gerrit


Jia, J. ; Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Computer Graphics and Interactive Techniques -SIGGRAPH-; European Association for Computer Graphics -EUROGRAPHICS-:
Web3D 2015, 20th International Conference on 3D Web Technology. Proceedings : June 18-21, 2015, Heraklion, Crete, Greece
New York: ACM, 2015
ISBN: 978-1-4503-3647-5
ISBN: 1-4503-3647-7
International Conference on 3D Web Technology (Web3D) <20, 2015, Heraklion>
Fraunhofer IGD ()
Computer Aided Design (CAD); WebGL; Extensible 3D (X3D); Business Field: Visual decision support; Business Field: Digital society; Research Area: Computer graphics (CG); Research Area: Computer vision (CV)

This paper presents the webVis / instant3DHub platform, which combines a novel Web-Components based framework and a Visual Computing as a Service infrastructure to deliver an interactive 3D data visualisation solution. The system focuses on minimising resource consumption, while maximising the end-user experience. It utilises an adaptive and automated combination of client, server and hybrid visualisation techniques, while orchestrating transmission, caching and rendering services to deliver structural and semantically complex data sets on any device class and network architecture. The API and Web Component framework allow the application developer to compose and manipulate complex data setups with a simple set of commands inside the browser, without requiring knowledge about the underlying service infrastructure, interfaces and the fully automated processes. This results in a new class of interactive applications, built around a canvas for real-time visualisation of massive data sets.