Interactive feature specification for focus+context visualization of complex simulation data
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Real-time Simulation and Visualization using Pre-calculated Fluid Simulator States
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BioAmbients: an abstraction for biological compartments
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The simulation project life-cycle: models and realities
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On the automation of computer network simulators
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IV '09 Proceedings of the 2009 13th International Conference Information Visualisation
Visual support for the understanding of simulation processes
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Bioinformatics
Space, time and visual analytics
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CiE'10 Proceedings of the Programs, proofs, process and 6th international conference on Computability in Europe
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PDMC-HIBI '10 Proceedings of the 2010 Ninth International Workshop on Parallel and Distributed Methods in Verification, and Second International Workshop on High Performance Computational Systems Biology
Exploring the performance of spatial stochastic simulation algorithms
Journal of Computational Physics
Point-Based Visualization for Large Hierarchies
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Visualization of Time-Oriented Data
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A graphical representation for biological processes in the stochastic pi-calculus
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The attributed pi-calculus with priorities
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Visual analysis of bipartite biological networks
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Using workflows in M&S software
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Visual Analytics is successfully employed for an integrated data analysis by means of combining visual and analytical methods. The starting point for current Visual Analytics tools and workflows is usually the readily available data set. Rarely though, Visual Analytics goes beyond the data set and also incorporates the data generating processes that have led to the data in the first place into the analysis. And indeed, in many use case scenarios, this is hardly possible, as these processes cannot be captured as data to be analyzed themselves. Yet for the applications, in which this is feasible, new opportunities and challenges open up. In this paper, we illustrate these opportunities and challenges by our efforts to bring together Visual Analytics and stochastic simulation for cell biological applications. The integration of both is possible, as the data generating process runs in silico and can thus be captured and analyzed alongside the mere simulation result. For this, we present solutions and tools, which permit Visual Analytics on all stages of this particular data generating process -- on the stages of the model, the experiment, the simulation runs, and a combination of all three.