Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science
A MapReduce approach to Gi*(d) spatial statistic
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
Spatial scene similarity assessment on Hadoop
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
A MapReduce workflow system for architecting scientific data intensive applications
Proceedings of the 2nd International Workshop on Software Engineering for Cloud Computing
Prediction-based auto-scaling of scientific workflows
Proceedings of the 9th International Workshop on Middleware for Grids, Clouds and e-Science
HyMR: a hybrid MapReduce workflow system
Proceedings of the 3rd international workshop on Emerging computational methods for the life sciences
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The growth of data used by data-intensive computations, e.g. Geographical Information Systems (GIS), has far outpaced the growth of the power of a single processor. The increasing demand of data-intensive applications calls for distributed computing. In this paper, we propose a high performance workflow system MRGIS, a parallel and distributed computing platform based on MapReduce clusters, to execute GIS applications efficiently. MRGIS consists of a design interface, a task scheduler, and a runtime support system. The design interface has two options: a GUI-based workflow designer and an API-based library for programming in Python. Given a GIS workflow, the scheduler analyzes data dependencies among tasks, then dispatches them to MapReduce clusters based on the current status of the system. Our experiment demonstrates that MRGIS can significantly improve the performance of GIS workflow execution.