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@ARTICLE{Holl:137751,
      author       = {Holl, Sonja and Zimmermann, Olav and Palmblad, Magnus and
                      Mohammed, Yassene and Hofmann-Apitius, Martin},
      title        = {{A} {N}ew {O}ptimization {P}hase for {S}cientific
                      {W}orkflow {M}anagement {S}ystems},
      journal      = {Future generation computer systems},
      volume       = {36},
      issn         = {0167-739X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2013-04072},
      pages        = {352-362},
      year         = {2014},
      abstract     = {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.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {412 - Grid Technologies and Infrastructures (POF2-412) /
                      411 - Computational Science and Mathematical Methods
                      (POF2-411)},
      pid          = {G:(DE-HGF)POF2-412 / G:(DE-HGF)POF2-411},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000336770700031},
      doi          = {10.1016/j.future.2013.09.005},
      url          = {https://juser.fz-juelich.de/record/137751},
}