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Title: Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows

Abstract

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. Here, 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.

Authors:
 [1];  [2];  [1];  [1];  [3];  [4];  [5];  [5];  [6];  [7];  [7];  [7];  [8]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Oregon, Eugene, OR (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Stony Brook Univ., NY (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
  5. Georgia Inst. of Technology, Atlanta, GA (United States)
  6. Univ. of Oregon, Eugene, OR (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  7. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  8. A*STAR Computational Resource Centre
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1379523
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016
Additional Journal Information:
Conference: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Chicago, IL (United States), 23-27 May 2016
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Data visualization; Data models; Computational modeling; Distributed databases; Plasmas; Middleware; Wide area networks

Citation Formats

Pugmire, David, Kress, James, Choi, Jong, Klasky, Scott, Kurc, Tahsin, Churchill, Randy Michael, Wolf, Matthew, Eisenhower, Greg, Childs, Hank, Wu, Kesheng, Sim, Alexander, Gu, Junmin, and Low, Jonathan. Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows. United States: N. p., 2016. Web. doi:10.1109/IPDPSW.2016.175.
Pugmire, David, Kress, James, Choi, Jong, Klasky, Scott, Kurc, Tahsin, Churchill, Randy Michael, Wolf, Matthew, Eisenhower, Greg, Childs, Hank, Wu, Kesheng, Sim, Alexander, Gu, Junmin, & Low, Jonathan. Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows. United States. doi:10.1109/IPDPSW.2016.175.
Pugmire, David, Kress, James, Choi, Jong, Klasky, Scott, Kurc, Tahsin, Churchill, Randy Michael, Wolf, Matthew, Eisenhower, Greg, Childs, Hank, Wu, Kesheng, Sim, Alexander, Gu, Junmin, and Low, Jonathan. Thu . "Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows". United States. doi:10.1109/IPDPSW.2016.175. https://www.osti.gov/servlets/purl/1379523.
@article{osti_1379523,
title = {Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows},
author = {Pugmire, David and Kress, James and Choi, Jong and Klasky, Scott and Kurc, Tahsin and Churchill, Randy Michael and Wolf, Matthew and Eisenhower, Greg and Childs, Hank and Wu, Kesheng and Sim, Alexander and Gu, Junmin and Low, Jonathan},
abstractNote = {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. Here, 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.},
doi = {10.1109/IPDPSW.2016.175},
journal = {Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016},
number = ,
volume = ,
place = {United States},
year = {Thu Aug 04 00:00:00 EDT 2016},
month = {Thu Aug 04 00:00:00 EDT 2016}
}

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