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Data Injection at Execution Time in Grid Environments Using Dynamic Data Driven Application System for Wildland Fire Spread Prediction

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3 Author(s)
Rodríguez, R. ; Comput. Archit. & Oper. Syst., Univ. Autonoma de Barcelona, Barcelona, Spain ; Cortés, A. ; Margalef, T.

In our research work, we use two Dynamic Data Driven Application System (DDDAS) methodologies to predict wildfire propagation. Our goal is to build a system that dynamically adapts to constant changes in environmental conditions when a hazard occurs and under strict real-time deadlines. For this purpose, we are on the way of building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time. In this paper, we propose a strategy for data injection in distributed environments.

Published in:

Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on

Date of Conference:

17-20 May 2010