Sungahn Ko, Jieqiong Zhao, Jing Xia, Shehzad Afzal, Xiaoyu Wang, Greg Abram, Niklas Elmqvist, Len Kne, David Riper, Kelly Gaither, Shaun Kennedy, William Tolone, William Ribarsky, David Ebert
Computational steering, visual analytics, critical infrastructure, homeland security.
We present VASA, a visual analytics platform consisting of a \ desktop application, a component model, and a suite of distributed \ simulation components for modeling the impact of societal threats \ such as weather, food contamination, and traffic on critical \ infrastructure such as supply chains, road networks, and power \ grids. \ Each component encapsulates a high-fidelity simulation model that \ together form an asynchronous simulation pipeline: a system of \ systems of individual simulations with a common data and parameter \ exchange format. \ At the heart of VASA is the Workbench, a visual analytics \ application providing three distinct features: (1) low-fidelity \ approximations of the distributed simulation components using local \ simulation proxies to enable analysts to interactively configure a \ simulation run; (2) computational steering mechanisms to manage the \ execution of individual simulation components; and (3) \ spatiotemporal and interactive methods to explore the combined \ results of a simulation run. \ We showcase the utility of the platform using examples involving \ supply chains during a hurricane as well as food contamination in a \ fast food restaurant chain.
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