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| Hauptseite > Publikationsdatenbank > A New Optimization Phase for Scientific Workflow Management Systems > print |
| Hauptseite > Publikationsdatenbank > A New Optimization Phase for Scientific Workflow Management Systems > print |
| 001 | 137751 | ||
| 005 | 20210129212104.0 | ||
| 024 | 7 | _ | |2 DOI |a 10.1016/j.future.2013.09.005 |
| 024 | 7 | _ | |2 WOS |a WOS:000336770700031 |
| 037 | _ | _ | |a FZJ-2013-04072 |
| 082 | _ | _ | |a 004 |
| 100 | 1 | _ | |0 P:(DE-Juel1)132139 |a Holl, Sonja |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a A New Optimization Phase for Scientific Workflow Management Systems |
| 260 | _ | _ | |a Amsterdam [u.a.] |b Elsevier Science |c 2014 |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1398774710_20156 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a article |2 DRIVER |
| 520 | _ | _ | |a Scientific 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. |
| 536 | _ | _ | |0 G:(DE-HGF)POF2-412 |a 412 - Grid Technologies and Infrastructures (POF2-412) |c POF2-412 |f POF II |x 0 |
| 536 | _ | _ | |0 G:(DE-HGF)POF2-411 |a 411 - Computational Science and Mathematical Methods (POF2-411) |c POF2-411 |f POF II |x 1 |
| 700 | 1 | _ | |0 P:(DE-Juel1)132307 |a Zimmermann, Olav |b 1 |u fzj |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Palmblad, Magnus |b 2 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Mohammed, Yassene |b 3 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Hofmann-Apitius, Martin |b 4 |
| 773 | _ | _ | |0 PERI:(DE-600)2020551-X |a 10.1016/j.future.2013.09.005 |p 352-362 |t Future generation computer systems |v 36 |x 0167-739X |y 2014 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/137751/files/FZJ-2013-04072.pdf |y Restricted |
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| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-HGF)0 |a Center for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands |b 2 |
| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-HGF)0 |a Center for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands |b 3 |
| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-HGF)0 |a Fraunhofer Institute for Algorithmsand Scientific Computing (SCAI) Schloss Birlinghoven, 53754 Sankt Augustin, Germany |b 4 |
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| 914 | 1 | _ | |y 2014 |
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