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

Lei Jiang, Gabrielle Allen, Qin Chen

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationScientific and Statistical Database Management - 23rd International Conference, SSDBM 2011, Proceedings
Pages579-580
Number of pages2
DOIs
StatePublished - Aug 11 2011
Externally publishedYes
Event23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011 - Portland, OR, United States
Duration: Jul 20 2011Jul 22 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6809 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011
CountryUnited States
CityPortland, OR
Period7/20/117/22/11

Keywords

  • automated workflow
  • data mining
  • scalability
  • severe-storm simulation
  • surrogate model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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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