Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science
Parallelizing XML data-streaming workflows via MapReduce
Journal of Computer and System Sciences
Data parallelism in bioinformatics workflows using Hydra
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
A MapReduce workflow system for architecting scientific data intensive applications
Proceedings of the 2nd International Workshop on Software Engineering for Cloud Computing
Distributed workflow-driven analysis of large-scale biological data using biokepler
Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities
Provenance for MapReduce-based data-intensive workflows
Proceedings of the 6th workshop on Workflows in support of large-scale science
Proceedings of the 2012 Joint EDBT/ICDT Workshops
A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds
Journal of Grid Computing
Performance evaluation of parallel strategies in public clouds: A study with phylogenomic workflows
Future Generation Computer Systems
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MapReduce has recently gained a lot of attention as a parallel programming model for scalable data-intensive business and scientific analysis. In order to benefit from this powerful programming model in a scientific workflow environment, we propose a MapReduce-enabled scientific workflow composition framework consisting of: i) a dataflow based scientific workflow model that separates the declaration of the workflow interface from the definition of its functional body; ii) a set of dataflow constructs, including Map, Reduce, Loop, and Conditional, and their composition semantics to enable MapReduce-style scientific workflows; iii) an XML-based scientific workflow specification language, called WSL, in which both Map and Reduce are fully composable with other dataflow constructs in both flat and hierarchical manners. Besides leveraging the power of MapReduce to the workflow level, our workflow composition framework is unique in that workflows are the only operands for composition; in this way, our approach elegantly solves the two-world problem of existing composition frameworks, in which composition needs to deal with both the world of tasks and the world of workflows. The proposed framework is implemented and a case study is conducted to validate our techniques.