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000137751 0247_ $$2DOI$$a10.1016/j.future.2013.09.005
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000137751 1001_ $$0P:(DE-Juel1)132139$$aHoll, Sonja$$b0$$eCorresponding author$$ufzj
000137751 245__ $$aA New Optimization Phase for Scientific Workflow Management Systems
000137751 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2014
000137751 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1398774710_20156
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000137751 520__ $$aScientific workflows have emerged as an important tool for combining computational power with data analysis for all scientific domains in e-science, especially in the life sciences. They help scientists to design and execute complex in silico experiments. However, with rising complexity it becomes increasingly impractical to optimize scientific workflows by trial and error. To address this issue, we propose to insert a new optimization phase into the common scientific workflow life cycle. This paper describes the design and implementation of an automated optimizationframework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a lifescience oriented workflow management system and oers a versatile programming interface (API), which enables easy integration of arbitrary optimization methods. We have used this API to develop an example plugin for parameter optimization that is based on a Genetic Algorithm. Two use cases taken from the areas of structural bioinformatics and proteomics demonstrate how our framework facilitates setup, execution, and monitoring of workflow parameter optimization in high performance computing e-science environments.
000137751 536__ $$0G:(DE-HGF)POF2-412$$a412 - Grid Technologies and Infrastructures (POF2-412)$$cPOF2-412$$fPOF II$$x0
000137751 536__ $$0G:(DE-HGF)POF2-411$$a411 - Computational Science and Mathematical Methods (POF2-411)$$cPOF2-411$$fPOF II$$x1
000137751 7001_ $$0P:(DE-Juel1)132307$$aZimmermann, Olav$$b1$$ufzj
000137751 7001_ $$0P:(DE-HGF)0$$aPalmblad, Magnus$$b2
000137751 7001_ $$0P:(DE-HGF)0$$aMohammed, Yassene$$b3
000137751 7001_ $$0P:(DE-HGF)0$$aHofmann-Apitius, Martin$$b4
000137751 773__ $$0PERI:(DE-600)2020551-X$$a10.1016/j.future.2013.09.005$$p352-362$$tFuture generation computer systems$$v36$$x0167-739X$$y2014
000137751 8564_ $$uhttps://juser.fz-juelich.de/record/137751/files/FZJ-2013-04072.pdf$$yRestricted
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000137751 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences
000137751 9141_ $$y2014
000137751 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132139$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
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000137751 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aCenter for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands$$b2
000137751 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aCenter for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands$$b3
000137751 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aFraunhofer Institute for Algorithmsand Scientific Computing (SCAI) Schloss Birlinghoven, 53754 Sankt Augustin, Germany$$b4
000137751 9132_ $$0G:(DE-HGF)POF3-512$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vData-Intensive Science and Federated Computing$$x0
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