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