Abstract

Data- and model-driven computer simulations are increasingly critical in many application domains. Yet, several critical data challenges remain in obtaining and leveraging simulations in decision making. Simulations may track 100s of parameters, spanning multiple layers and spatialtemporal frames, affected by complex inter-dependent dynamic processes. Moreover, due to the large numbers of unknowns, decision makers usually need to generate ensembles of stochastic realizations, requiring 10s-1000s of individual simulation instances. The situation on the ground evolves unpredictably, requiring continuously adaptive simulation ensembles. We introduce the DataStorm framework for simulation ensemble management, and demonstrate its DataStorm-FE data- and decisionow and coordination engine for creating and maintaining coupled, multi-model simulation ensembles. DataStorm-FE enables end-to-end ensemble planning and optimization, including parameter-space sampling, output aggregation and alignment, and state and provenance data management, to improve the overall simulation process. It also aims to work effciently, producing results while working within a limited simulation budget, and incorporates a multivariate, spatiotemporal data browser to empower decision-making based on these improved results.

Original languageEnglish (US)
Pages (from-to)1906-1909
Number of pages4
JournalProceedings of the VLDB Endowment
Volume11
Issue number12
DOIs
Publication statusPublished - Jan 1 2017
Event44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil
Duration: Aug 27 2017Aug 31 2017

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ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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