Web-based visual analytics for extreme scale climate science

Chad A. Steed; Katherine J. Evans; John F. Harney; Brian C. Jewell; Galen Shipman; Brian E. Smith; Peter E. Thornton; Dean N. Williams

doi:10.1109/BigData.2014.7004255

Web-based visual analytics for extreme scale climate science

Chad A. Steed, Katherine J. Evans, John F. Harney, Brian C. Jewell, Galen Shipman, Brian E. Smith, Peter E. Thornton, Dean N. Williams

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

11 Scopus citations

Abstract

In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, inefficient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via new visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.

Original language	English
Title of host publication	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
Editors	Jimmy Lin, Jian Pei, Xiaohua Tony Hu, Wo Chang, Raghunath Nambiar, Charu Aggarwal, Nick Cercone, Vasant Honavar, Jun Huan, Bamshad Mobasher, Saumyadipta Pyne
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	383-392
Number of pages	10
ISBN (Electronic)	9781479956654
DOIs	https://doi.org/10.1109/BigData.2014.7004255
State	Published - 2014
Event	2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States Duration: Oct 27 2014 → Oct 30 2014

Publication series

Name	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Conference

Conference	2nd IEEE International Conference on Big Data, IEEE Big Data 2014
Country/Territory	United States
City	Washington
Period	10/27/14 → 10/30/14

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10.1109/BigData.2014.7004255

Cite this

Steed, C. A., Evans, K. J., Harney, J. F., Jewell, B. C., Shipman, G., Smith, B. E., Thornton, P. E., & Williams, D. N. (2014). Web-based visual analytics for extreme scale climate science. In J. Lin, J. Pei, X. T. Hu, W. Chang, R. Nambiar, C. Aggarwal, N. Cercone, V. Honavar, J. Huan, B. Mobasher, & S. Pyne (Eds.), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 (pp. 383-392). Article 7004255 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004255

Steed, Chad A. ; Evans, Katherine J. ; Harney, John F. et al. / Web-based visual analytics for extreme scale climate science. Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. editor / Jimmy Lin ; Jian Pei ; Xiaohua Tony Hu ; Wo Chang ; Raghunath Nambiar ; Charu Aggarwal ; Nick Cercone ; Vasant Honavar ; Jun Huan ; Bamshad Mobasher ; Saumyadipta Pyne. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 383-392 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

@inproceedings{5e976e23008f4fd3b0f5c7bec9eded37,

title = "Web-based visual analytics for extreme scale climate science",

abstract = "In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, inefficient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via new visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.",

author = "Steed, {Chad A.} and Evans, {Katherine J.} and Harney, {John F.} and Jewell, {Brian C.} and Galen Shipman and Smith, {Brian E.} and Thornton, {Peter E.} and Williams, {Dean N.}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE International Conference on Big Data, IEEE Big Data 2014 ; Conference date: 27-10-2014 Through 30-10-2014",

year = "2014",

doi = "10.1109/BigData.2014.7004255",

language = "English",

series = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "383--392",

editor = "Jimmy Lin and Jian Pei and Hu, {Xiaohua Tony} and Wo Chang and Raghunath Nambiar and Charu Aggarwal and Nick Cercone and Vasant Honavar and Jun Huan and Bamshad Mobasher and Saumyadipta Pyne",

booktitle = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",

}

Steed, CA , Evans, KJ, Harney, JF, Jewell, BC, Shipman, G, Smith, BE, Thornton, PE & Williams, DN 2014, Web-based visual analytics for extreme scale climate science. in J Lin, J Pei, XT Hu, W Chang, R Nambiar, C Aggarwal, N Cercone, V Honavar, J Huan, B Mobasher & S Pyne (eds), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014., 7004255, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, Institute of Electrical and Electronics Engineers Inc., pp. 383-392, 2nd IEEE International Conference on Big Data, IEEE Big Data 2014, Washington, United States, 10/27/14. https://doi.org/10.1109/BigData.2014.7004255

Web-based visual analytics for extreme scale climate science. / Steed, Chad A.; Evans, Katherine J.; Harney, John F. et al.
Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. ed. / Jimmy Lin; Jian Pei; Xiaohua Tony Hu; Wo Chang; Raghunath Nambiar; Charu Aggarwal; Nick Cercone; Vasant Honavar; Jun Huan; Bamshad Mobasher; Saumyadipta Pyne. Institute of Electrical and Electronics Engineers Inc., 2014. p. 383-392 7004255 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

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

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AU - Evans, Katherine J.

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AU - Jewell, Brian C.

AU - Shipman, Galen

AU - Smith, Brian E.

AU - Thornton, Peter E.

AU - Williams, Dean N.

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N2 - In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, inefficient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via new visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.

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A2 - Lin, Jimmy

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A2 - Chang, Wo

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A2 - Cercone, Nick

A2 - Honavar, Vasant

A2 - Huan, Jun

A2 - Mobasher, Bamshad

A2 - Pyne, Saumyadipta

PB - Institute of Electrical and Electronics Engineers Inc.

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Steed CA , Evans KJ, Harney JF, Jewell BC, Shipman G, Smith BE et al. Web-based visual analytics for extreme scale climate science. In Lin J, Pei J, Hu XT, Chang W, Nambiar R, Aggarwal C, Cercone N, Honavar V, Huan J, Mobasher B, Pyne S, editors, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 383-392. 7004255. (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). doi: 10.1109/BigData.2014.7004255

Web-based visual analytics for extreme scale climate science

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