Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Modeling master/worker applications for automatic performance tuning
Parallel Computing - Algorithmic skeletons
An Adaptive System for Forest Fire Behavior Prediction
CSE '08 Proceedings of the 2008 11th IEEE International Conference on Computational Science and Engineering
Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Injecting Dynamic Real-Time Data into a DDDAS for Forest Fire Behavior Prediction
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Future Generation Computer Systems
S2F2M: statistical system for forest fire management
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Simulation of surface fire fronts using fireLib and GPUs
Environmental Modelling & Software
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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.