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Proceedings Paper

Why high performance visual data analytics is both relevant and difficult
Author(s): E. Wes Bethel; Prabhat Prabhat; Suren Byna; Oliver Rübel; K. John Wu; Michael Wehner
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Paper Abstract

Data visualization, as well as data analysis and data analytics, are all an integral part of the scientific process. Collectively, these technologies provide the means to gain insight into data of ever-increasing size and complexity. Over the past two decades, a substantial amount of visualization, analysis, and analytics R&D has focused on the challenges posed by increasing data size and complexity, as well as on the increasing complexity of a rapidly changing computational platform landscape. While some of this research focuses on solely on technologies, such as indexing and searching or novel analysis or visualization algorithms, other R&D projects focus on applying technological advances to specific application problems. Some of the most interesting and productive results occur when these two activities-R&D and application-are conducted in a collaborative fashion, where application needs drive R&D, and R&D results are immediately applicable to real-world problems.

Paper Details

Date Published: 4 February 2013
PDF: 10 pages
Proc. SPIE 8654, Visualization and Data Analysis 2013, 86540B (4 February 2013); doi: 10.1117/12.2010980
Show Author Affiliations
E. Wes Bethel, Lawrence Berkeley National Lab. (United States)
Prabhat Prabhat, Lawrence Berkeley National Lab. (United States)
Suren Byna, Lawrence Berkeley National Lab. (United States)
Oliver Rübel, Lawrence Berkeley National Lab. (United States)
K. John Wu, Lawrence Berkeley National Lab. (United States)
Michael Wehner, Lawrence Berkeley National Lab. (United States)


Published in SPIE Proceedings Vol. 8654:
Visualization and Data Analysis 2013
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen, Editor(s)

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