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2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (2016)
Chicago, IL, USA
May 23, 2016 to May 27, 2016
ISBN: 978-1-5090-3683-7
pp: 1007-1013
Data driven science is becoming increasingly more common, complex, and is placing tremendous stresses on visualization and analysis frameworks. Data sources producing 10GB per second (and more) are becoming increasingly commonplace in both simulation, sensor and experimental sciences. These data sources, which are often distributed around the world, must be analyzed by teams of scientists that are also distributed. Enabling scientists to view, query and interact with such large volumes of data in near-real-time requires a rich fusion of visualization and analysis techniques, middleware and workflow systems. This paper discusses initial research into visualization and analysis of distributed data workflows that enables scientists to make near-real-time decisions of large volumes of time varying data.
Data visualization, Data models, Computational modeling, Distributed databases, Plasmas, Middleware, Wide area networks
David Pugmire, James Kress, Jong Choi, Scott Klasky, Tahsin Kurc, Randy Michael Churchill, Matthew Wolf, Greg Eisenhower, Hank Childs, Kesheng Wu, Alexander Sim, Junmin Gu, Jonathan Low, "Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows", 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), vol. 00, no. , pp. 1007-1013, 2016, doi:10.1109/IPDPSW.2016.175
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