Visualization and analysis for near-real-time decision making in distributed workflows

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

doi:10.1109/IPDPSW.2016.175

Visualization and analysis for near-real-time decision making in distributed workflows

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1007-1013
Number of pages	7
ISBN (Electronic)	9781509021406
DOIs	https://doi.org/10.1109/IPDPSW.2016.175
State	Published - Jul 18 2016
Event	30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016 - Chicago, United States Duration: May 23 2016 → May 27 2016

Publication series

Name	Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016

Conference

Conference	30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016
Country/Territory	United States
City	Chicago
Period	05/23/16 → 05/27/16

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10.1109/IPDPSW.2016.175

Cite this

Pugmire, D., Kress, J., Choi, J., Klasky, S., Kurc, T., Churchill, R. M., Wolf, M., Eisenhower, G., Childs, H., Wu, K., Sim, A., Gu, J., & Low, J. (2016). Visualization and analysis for near-real-time decision making in distributed workflows. In Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016 (pp. 1007-1013). Article 7529972 (Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPSW.2016.175

Pugmire, David ; Kress, James ; Choi, Jong et al. / Visualization and analysis for near-real-time decision making in distributed workflows. Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1007-1013 (Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016).

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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.",

author = "David Pugmire and James Kress and Jong Choi and Scott Klasky and Tahsin Kurc and Churchill, {Randy Michael} and Matthew Wolf and Greg Eisenhower and Hank Childs and Kesheng Wu and Alexander Sim and Junmin Gu and Jonathan Low",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016 ; Conference date: 23-05-2016 Through 27-05-2016",

year = "2016",

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doi = "10.1109/IPDPSW.2016.175",

language = "English",

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Pugmire, D, Kress, J, Choi, J , Klasky, S, Kurc, T, Churchill, RM, Wolf, M, Eisenhower, G, Childs, H, Wu, K, Sim, A, Gu, J & Low, J 2016, Visualization and analysis for near-real-time decision making in distributed workflows. in Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016., 7529972, Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1007-1013, 30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016, Chicago, United States, 05/23/16. https://doi.org/10.1109/IPDPSW.2016.175

Visualization and analysis for near-real-time decision making in distributed workflows. / Pugmire, David; Kress, James; Choi, Jong et al.
Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1007-1013 7529972 (Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Pugmire D, Kress J, Choi J , Klasky S, Kurc T, Churchill RM et al. Visualization and analysis for near-real-time decision making in distributed workflows. In Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1007-1013. 7529972. (Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016). doi: 10.1109/IPDPSW.2016.175

Visualization and analysis for near-real-time decision making in distributed workflows

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