Data injection at execution time in grid environments using Dynamic Data Driven Application System for wildland fire spread prediction

Roque Rodríguez*, Ana Cortés, Tomás Margalef

*Corresponding author for this work

Research output: Chapter in BookChapterResearchpeer-review

4 Citations (Scopus)

Abstract

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 indistributed environments.

Original languageAmerican English
Title of host publicationCCGrid 2010 - 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing
Pages565-568
Number of pages4
DOIs
Publication statusPublished - 2010

Publication series

NameCCGrid 2010 - 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing

Keywords

  • Dynamic Data Driven Application System
  • Evolutionary computing
  • Forest fire prediction
  • High performance computing
  • Parallel computing
  • Parallel simulation
  • Real-time data injection

Fingerprint Dive into the research topics of 'Data injection at execution time in grid environments using Dynamic Data Driven Application System for wildland fire spread prediction'. Together they form a unique fingerprint.

Cite this