Static scheduling of synchronous data flow programs for digital signal processing
IEEE Transactions on Computers
Building Cyberinfrastructure for Bioinformatics Using Service Oriented Architecture
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Taverna: lessons in creating a workflow environment for the life sciences: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Scientific workflow management and the Kepler system: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Opal: SimpleWeb Services Wrappers for Scientific Applications
ICWS '06 Proceedings of the IEEE International Conference on Web Services
A Task Abstraction and Mapping Approach to the Shimming Problem in Scientific Workflows
SCC '09 Proceedings of the 2009 IEEE International Conference on Services Computing
Weaver: integrating distributed computing abstractions into scientific workflows using Python
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
P-GRADE Portal: A generic workflow system to support user communities
Future Generation Computer Systems
Bioinformatics
Bioinformatics
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To be easily constructed, shared and maintained, complex in silico bioinformatics analysis are structured as workflows. Furthermore, the growth of computational power and storage demand from this domain, requires workflows to be efficiently executed. However, workflow performances usually rely on the ability of the designer to extract potential parallelism. But atomic bioinformatics tasks do not often exhibit direct parallelism which may appears later in the workflow design process. In this paper, we propose a Model-Driven Architecture approach for capturing the complete design process of bioinformatics workflows. More precisely, two workflow models are specified: the first one, called design model, graphically captures a low throughput prototype. The second one, called execution model, specifies multiple levels of coarse grained parallelism. The execution model is automatically generated from the design model using annotation derived from the EDAM ontology. These annotations describe the data types connecting differents elementary tasks. The execution model can then be interpreted by a workflow engine and executed on hardware having intensive computation facility.