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Visualizing Large-Scale Datasets: Challenges and Opportunities
Despite unprecedented growth in the volume of data from both computational simulations and instrument/sensor sources, our ability to manipulate, explore, and understand large datasets is lagging behind. Visualization transforms raw data into vivid 2D or 3D images that help scientists reveal important features and trends in the data, convey ideas, and communicate their findings. However, the massive data volumes create new challenges for visualization researchers and industry, and make previous visualization approaches impractical. The new generation of visualization methods must scale well with the growing data volumes and cope with other parts of the data analysis pipeline, such as storage and display devices.
To accelerate development of new data manipulation and visualization methods for massive datasets, the National Science Foundation and the US Department of Energy have sponsored a series of workshops on relevant topics. This panel discussed the data and visualization concepts that have emerged from the workshop series, including innovations in data handling, representations, telepresence, and visualization.
Kwan-Liu Ma
University of California, Davis
John Van Rosendale
US Department of Energy
Stephen Eick
Visual Insights/Lucent Technologies
Bernd Hamann
University of California, Davis
Philip Heermann
Sandia National Laboratory
Christopher Johnson
University of Utah
Mike Krogh
Computational Engineering International, Inc.
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