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 language | English (US) |
|---|---|
| Pages (from-to) | 1906-1909 |
| Number of pages | 4 |
| Journal | Proceedings of the VLDB Endowment |
| Volume | 11 |
| Issue number | 12 |
| DOIs | |
| State | Published - Jan 1 2017 |
| Event | 44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil Duration: Aug 27 2017 → Aug 31 2017 |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science(all)
Cite this
DataStormFE : A Data-and decision-flow and coordination engine for coupled simulation ensembles. / Behrens, Hans Walter; Candan, Kasim; Chen, Xilun; Gadkari, Ashish; Garg, Yash; Li, Mao Lin.
In: Proceedings of the VLDB Endowment, Vol. 11, No. 12, 01.01.2017, p. 1906-1909.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - DataStormFE
T2 - A Data-and decision-flow and coordination engine for coupled simulation ensembles
AU - Behrens, Hans Walter
AU - Candan, Kasim
AU - Chen, Xilun
AU - Gadkari, Ashish
AU - Garg, Yash
AU - Li, Mao Lin
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85058882679&partnerID=8YFLogxK
U2 - 10.14778/3229863.3236221
DO - 10.14778/3229863.3236221
M3 - Conference article
AN - SCOPUS:85058882679
VL - 11
SP - 1906
EP - 1909
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
SN - 2150-8097
IS - 12
ER -