Scalable and automated workflow in mining large-scale severe-storm simulations

Lei Jiang; Gabrielle Allen; Qin Chen

doi:10.1007/978-3-642-22351-8_45

Scalable and automated workflow in mining large-scale severe-storm simulations

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

Abstract

The simulation of large-scale complex systems, such as modeling the effects of hurricanes or storms in coastal environments, typically requires a large amount of computing resources in addition to data storage capacity. To make an efficient prediction of the potential storm surge height for an incoming hurricane, surrogate models, which are computationally cheap and can reach a comparable level of accuracy with simulations, are desired. In this paper, we present a scalable and automated workflow for surrogate modeling with hurricane-related simulation data.

Original language	English (US)
Title of host publication	Scientific and Statistical Database Management - 23rd International Conference, SSDBM 2011, Proceedings
Pages	579-580
Number of pages	2
DOIs	https://doi.org/10.1007/978-3-642-22351-8_45
State	Published - Aug 11 2011
Externally published	Yes
Event	23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011 - Portland, OR, United States Duration: Jul 20 2011 → Jul 22 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6809 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011
Country	United States
City	Portland, OR
Period	7/20/11 → 7/22/11

Keywords

automated workflow
data mining
scalability
severe-storm simulation
surrogate model

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-22351-8_45

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Cite this

Jiang, L., Allen, G., & Chen, Q. (2011). Scalable and automated workflow in mining large-scale severe-storm simulations. In Scientific and Statistical Database Management - 23rd International Conference, SSDBM 2011, Proceedings (pp. 579-580). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6809 LNCS). https://doi.org/10.1007/978-3-642-22351-8_45

Scalable and automated workflow in mining large-scale severe-storm simulations. / Jiang, Lei; Allen, Gabrielle; Chen, Qin.

Scientific and Statistical Database Management - 23rd International Conference, SSDBM 2011, Proceedings. 2011. p. 579-580 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6809 LNCS).

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

Jiang, L, Allen, G & Chen, Q 2011, Scalable and automated workflow in mining large-scale severe-storm simulations. in Scientific and Statistical Database Management - 23rd International Conference, SSDBM 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6809 LNCS, pp. 579-580, 23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011, Portland, OR, United States, 7/20/11. https://doi.org/10.1007/978-3-642-22351-8_45

Jiang L, Allen G, Chen Q. Scalable and automated workflow in mining large-scale severe-storm simulations. In Scientific and Statistical Database Management - 23rd International Conference, SSDBM 2011, Proceedings. 2011. p. 579-580. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-22351-8_45

Jiang, Lei ; Allen, Gabrielle ; Chen, Qin. / Scalable and automated workflow in mining large-scale severe-storm simulations. Scientific and Statistical Database Management - 23rd International Conference, SSDBM 2011, Proceedings. 2011. pp. 579-580 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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