SIMDMS: Data management and analysis to support decision making through large simulation ensembles

Silvestro Poccia, Maria Luisa Sapino, Sicong Liu, Xilun Chen, Yash Garg, Shengyu Huang, Jung Hyun Kim, Xinsheng Li, Parth Nagarkar, Kasim Candan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Data- and model-driven computer simulations are increasingly critical in many application domains. These simulations may track 100s or 1000s of inter-dependent parameters, spanning multiple layers and spatial-temporal frames, affected by complex dynamic processes operating at different resolutions. Because of the size and complexity of the data and the varying spatial and temporal scales at which the key processes operate, experts often lack the means to analyze results of large simulation ensembles, understand relevant processes, and assess the robustness of conclusions driven from the resulting simulations. Moreover, data and models dynamically evolve over time requiring continuous adaptation of simulation ensembles. The simDMS platform aims to address the key challenges underlying the creation and use of large simulation ensembles and enables (a) execution, storage, and indexing of large ensemble simulation data sets and the corresponding models; and (b) search, analysis, and exploration of ensemble simulation data sets to enable ensemble-based decision support.

Original languageEnglish (US)
Title of host publicationAdvances in Database Technology - EDBT 2017
Subtitle of host publication20th International Conference on Extending Database Technology, Proceedings
PublisherOpenProceedings.org
Pages582-585
Number of pages4
Volume2017-March
ISBN (Electronic)9783893180738
DOIs
StatePublished - Jan 1 2017
Event20th International Conference on Extending Database Technology, EDBT 2017 - Venice, Italy
Duration: Mar 21 2017Mar 24 2017

Other

Other20th International Conference on Extending Database Technology, EDBT 2017
CountryItaly
CityVenice
Period3/21/173/24/17

Keywords

  • Multivariate time series
  • Simulation ensembles

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Computer Science Applications

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  • Cite this

    Poccia, S., Sapino, M. L., Liu, S., Chen, X., Garg, Y., Huang, S., Kim, J. H., Li, X., Nagarkar, P., & Candan, K. (2017). SIMDMS: Data management and analysis to support decision making through large simulation ensembles. In Advances in Database Technology - EDBT 2017: 20th International Conference on Extending Database Technology, Proceedings (Vol. 2017-March, pp. 582-585). OpenProceedings.org. https://doi.org/10.5441/002/edbt.2017.75