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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. 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:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1];  [2]; ORCiD logo [1];  [3];  [4];  [5];  [5];  [5];  [6]
  1. ORNL
  2. Princeton Plasma Physics Laboratory (PPPL)
  3. Georgia Institute of Technology, Atlanta
  4. University of Oregon
  5. Lawrence Berkeley National Laboratory (LBNL)
  6. A*Star Computational Resource Centre
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1558497
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Parallel and Distributed Processing Symposium Workshops (IPDPSW) - Chicago, Illinois, United States of America - 5/23/2016 8:00:00 AM-5/27/2016 4:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Pugmire, Dave, Kress, James M., Choi, Jong Youl, Klasky, Scott A., Kurc, Tahsin M., Churchill, Michael, Wolf, Matthew D., Eisenhauer, 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, Dave, Kress, James M., Choi, Jong Youl, Klasky, Scott A., Kurc, Tahsin M., Churchill, Michael, Wolf, Matthew D., Eisenhauer, 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, Dave, Kress, James M., Choi, Jong Youl, Klasky, Scott A., Kurc, Tahsin M., Churchill, Michael, Wolf, Matthew D., Eisenhauer, Greg, Childs, Hank, Wu, Kesheng, Sim, Alexander, Gu, Junmin, and Low, Jonathan. Sun . "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/1558497.
@article{osti_1558497,
title = {Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows},
author = {Pugmire, Dave and Kress, James M. and Choi, Jong Youl and Klasky, Scott A. and Kurc, Tahsin M. and Churchill, Michael and Wolf, Matthew D. and Eisenhauer, 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. 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 = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {5}
}

Conference:
Other availability
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