Journal Paper

#34 

@article{Ko2014, title={VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure}, author={Sungahn Ko and Jieqiong Zhao and Jing Xia and Shehzad Afzal and Xiaoyu Wang and Greg Abram and Niklas Elmqvist and Len Kne and David Van Riper and Kelly Gaither and Shaun Kennedy and William Tolone and William Ribarsky and David S. Ebert}, url={http://www.umiacs.umd.edu/~elm/projects/vasa/vasa.pdf}, year={2014}, date={2014-11-13}, journal={IEEE Transactions on Visualization & Computer Graphics}, volume={20}, number={12}, pages={1853--1862}, abstract={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 theWorkbench, 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.}, keywords={}, }

Journal Paper

#26 

@article{Afzal2012, title={Spatial Text Visualization Using Automatic Typographic Maps}, author={Shehzad Afzal and Ross Maciejewski and Yun Jang and Niklas Elmqvist and David S. Ebert}, url={http://www.umiacs.umd.edu/~elm/projects/typomapvis/typomapvis.pdf}, year={2012}, date={2012-01-01}, journal={IEEE Computer Graphics and Applications (Proc. Vis/InfoVis 2012)}, volume={18}, number={12}, pages={2556-2564}, abstract={We present a method for automatically building typographic maps that merge text and spatial data into a visual representation where text alone forms the graphical features. We further show how to use this approach to visualize spatial data such as traffic density, crime rate, or demographic data. The technique accepts a vector representation of a geographic map and spatializes the textual labels in the space onto polylines and polygons based on user-defined visual attributes and constraints. Our sample implementation runs as a Web service, spatializing shape files from the OpenStreetMap project into typographic maps for any region.}, keywords={}, }