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== Characteristics of Data-Intensive Computing Systems ==
The [[National Science Foundation]] believes that data-intensive computing requires a “fundamentally different set of principles” than current computing approaches.<ref>[http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503324&org=IIS Data-Intensive Computing] by NSF. "Data-Intensive Computing," 2009.</ref> Through a funding program within the Computer and Information Science and Engineering area, the NSF is seeking to “increase understanding of the capabilities and limitations of data-intensive computing.” The key areas of focus are:
* Approaches to [[parallel programming]] to address the [[parallel processing]] of data on data-intensive systems
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* Identifying applications that can exploit this computing paradigm and determining how it should evolve to support emerging data-intensive applications
[[Pacific Northwest National Labs]] has defined data-intensive computing as “capturing, managing, analyzing, and understanding data at volumes and rates that push the frontiers of current technologies”.<ref>[http://www.cs.cmu.edu/~bryant/presentations/DISC-concept.ppt Data Intensive Computing] by PNNL. "Data Intensive Computing," 2008</ref><ref>[http://www.computer.org/portal/web/csdl/doi/10.1109/MC.2009.26 The Changing Paradigm of Data-Intensive Computing] by R.T. Kouzes, G.A. Anderson, S.T. Elbert, I. Gorton, and D.K. Gracio, "The Changing Paradigm of Data-Intensive Computing," Computer, Vol. 42, No. 1, 2009, pp. 26-3</ref> They believe that to address the rapidly growing data volumes and complexity requires “epochal advances in software, hardware, and algorithm development” which can scale readily with size of the data and provide effective and timely analysis and processing results.
== Processing Approach ==
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