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@ARTICLE{Memon:817851,
      author       = {Memon, Mohammad Shahbaz and Riedel, Morris and Memon, Ahmed
                      and Koeritz, Chris and Grimshaw, Andrew and Neukirchen,
                      Helmut},
      title        = {{E}nabling {S}calable {D}ata {P}rocessing and {M}anagement
                      through {S}tandards-based {J}ob {E}xecution and the {G}lobal
                      {F}ederated {F}ile {S}ystem},
      journal      = {Scalable computing},
      volume       = {17},
      number       = {2},
      issn         = {1895-1767},
      reportid     = {FZJ-2016-04468},
      pages        = {115-128},
      year         = {2016},
      abstract     = {Emerging challenges for scientific communities are to
                      efficiently process big data obtained by experimentation and
                      computational simulations. Supercomputing architectures are
                      available to support scalable and high performant processing
                      environment, but many of the existing algorithm
                      implementations are still unable to cope with its
                      architectural complexity. One approach is to have innovative
                      technologies that effectively use these resources and also
                      deal with geographically dispersed large datasets. Those
                      technologies should be accessible in a way that data
                      scientists who are running data intensive computations do
                      not have to deal with technical intricacies of the underling
                      execution system. Our work primarily focuses on providing
                      data scientists with transparent access to these resources
                      in order to easily analyze data. Impact of our work is given
                      by describing how we enabled access to multiple high
                      performance computing resources through an open
                      standards-based middleware that takes advantage of a unified
                      data management provided by the the Global Federated File
                      System. Our architectural design and its associated
                      implementation is validated by a usecase that requires
                      massivley parallel DBSCAN outlier detection on a 3D point
                      clouds dataset.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512)},
      pid          = {G:(DE-HGF)POF3-512},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000377906100006},
      doi          = {10.12694/scpe.v17i2.1160},
      url          = {https://juser.fz-juelich.de/record/817851},
}