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Proceedings Article

Why high performance visual data analytics is both relevant and difficult

[+] Author Affiliations
E. Wes Bethel, Prabhat Prabhat, Suren Byna, Oliver Rübel, K. John Wu, Michael Wehner

Lawrence Berkeley National Lab. (United States)

Proc. SPIE 8654, Visualization and Data Analysis 2013, 86540B (February 4, 2013); doi:10.1117/12.2010980
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From Conference Volume 8654

  • Visualization and Data Analysis 2013
  • Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen; Christopher G. Healey
  • Burlingame, California, USA | February 03, 2013

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. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

E. Wes Bethel ; Prabhat Prabhat ; Suren Byna ; Oliver Rübel ; K. John Wu, et al.
" Why high performance visual data analytics is both relevant and difficult ", Proc. SPIE 8654, Visualization and Data Analysis 2013, 86540B (February 4, 2013); doi:10.1117/12.2010980; http://dx.doi.org/10.1117/12.2010980


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