A new optimization phase for scientific workflow management systems
Scientific workflows have emerged as an important tool for combining the 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 becom...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Holl, Sonja [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Surgeon-patient matching based on pairwise comparisons information for elective surgery - Jiang, Yan-Ping ELSEVIER, 2020, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:36 ; year:2014 ; pages:352-362 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.future.2013.09.005 |
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| 520 | |a Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. | ||
| 520 | |a Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. | ||
| 650 | 7 | |a Workflow optimization |2 Elsevier | |
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| 700 | 1 | |a Zimmermann, Olav |4 oth | |
| 700 | 1 | |a Palmblad, Magnus |4 oth | |
| 700 | 1 | |a Mohammed, Yassene |4 oth | |
| 700 | 1 | |a Hofmann-Apitius, Martin |4 oth | |
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10.1016/j.future.2013.09.005 doi GBVA2014004000011.pica (DE-627)ELV027868885 (ELSEVIER)S0167-739X(13)00184-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 004 VZ 85.35 bkl 54.80 bkl Holl, Sonja verfasserin aut A new optimization phase for scientific workflow management systems 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. Workflow optimization Elsevier e-science Elsevier Genetic algorithm Elsevier Taverna Elsevier Workflow life cycle Elsevier Zimmermann, Olav oth Palmblad, Magnus oth Mohammed, Yassene oth Hofmann-Apitius, Martin oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:36 year:2014 pages:352-362 extent:11 https://doi.org/10.1016/j.future.2013.09.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 36 2014 352-362 11 045F 004 |
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10.1016/j.future.2013.09.005 doi GBVA2014004000011.pica (DE-627)ELV027868885 (ELSEVIER)S0167-739X(13)00184-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 004 VZ 85.35 bkl 54.80 bkl Holl, Sonja verfasserin aut A new optimization phase for scientific workflow management systems 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. Workflow optimization Elsevier e-science Elsevier Genetic algorithm Elsevier Taverna Elsevier Workflow life cycle Elsevier Zimmermann, Olav oth Palmblad, Magnus oth Mohammed, Yassene oth Hofmann-Apitius, Martin oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:36 year:2014 pages:352-362 extent:11 https://doi.org/10.1016/j.future.2013.09.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 36 2014 352-362 11 045F 004 |
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10.1016/j.future.2013.09.005 doi GBVA2014004000011.pica (DE-627)ELV027868885 (ELSEVIER)S0167-739X(13)00184-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 004 VZ 85.35 bkl 54.80 bkl Holl, Sonja verfasserin aut A new optimization phase for scientific workflow management systems 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. Workflow optimization Elsevier e-science Elsevier Genetic algorithm Elsevier Taverna Elsevier Workflow life cycle Elsevier Zimmermann, Olav oth Palmblad, Magnus oth Mohammed, Yassene oth Hofmann-Apitius, Martin oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:36 year:2014 pages:352-362 extent:11 https://doi.org/10.1016/j.future.2013.09.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 36 2014 352-362 11 045F 004 |
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10.1016/j.future.2013.09.005 doi GBVA2014004000011.pica (DE-627)ELV027868885 (ELSEVIER)S0167-739X(13)00184-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 004 VZ 85.35 bkl 54.80 bkl Holl, Sonja verfasserin aut A new optimization phase for scientific workflow management systems 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. Workflow optimization Elsevier e-science Elsevier Genetic algorithm Elsevier Taverna Elsevier Workflow life cycle Elsevier Zimmermann, Olav oth Palmblad, Magnus oth Mohammed, Yassene oth Hofmann-Apitius, Martin oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:36 year:2014 pages:352-362 extent:11 https://doi.org/10.1016/j.future.2013.09.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 36 2014 352-362 11 045F 004 |
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Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. |
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Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. |
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Scientific workflows have emerged as an important tool for combining the 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 optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers 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. |
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