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@PHDTHESIS{Memon:865715,
      author       = {Memon, Mohammad Shahbaz},
      title        = {{S}tandards-based {M}odels and {A}rchitectures to
                      {A}utomate {S}calable and {D}istributed {D}ata {P}rocessing
                      and {A}nalysis},
      school       = {University of Iceland},
      type         = {Dissertation},
      reportid     = {FZJ-2019-05050},
      pages        = {102 pp.},
      year         = {2019},
      note         = {Dissertation, University of Iceland, 2019},
      abstract     = {Scientific communities engaging in big data analysis face
                      numerous challenges in managing complex computations and the
                      related data on emerging and distributed computing
                      infrastructures. Large-scale data analysis requires
                      applications with simplified access to multiple resource
                      management systems. Several generic or domain-specific
                      technologies have been developed to exploit diversified
                      computing environments, but due to the heterogeneity of
                      computing and data architectures they are not capable of
                      enabling real science cases. Scientific gateways and
                      workflows are one such example which requires the management
                      of jobs on multiple kinds of batch systems using
                      heterogeneous supercomputing architectures and access to
                      advanced distributed file systems. To support these
                      requirements, a unified architectural framework is presented
                      in this dissertation that coalesces the right combination of
                      standards and adequate middleware realisation. This
                      framework manages concurrent access for diversified user
                      communities through consistent and robust computing and data
                      interfaces oriented to current application and
                      infrastructure demands. The investigations reported in this
                      dissertation were mainly motivated by physical and
                      machine-learning models, represented by two scientific case
                      studies: biophysics and Earth sciences. In the field of
                      biophysics, the UltraScan scientific gateway is enhanced to
                      enable the processing of domain-specific data through
                      standards-based job and data management interfaces in HPC
                      environments. The second domain deals with Earth sciences
                      and automates the processing of machine-learning algorithms
                      (e.g. classification of remote sensing images) using
                      scalable and parallel implementations. As proof of concept,
                      both the case studies are supported through open source
                      implementations, in the form of middleware realisation,
                      client APIs and their integration with state-of-the-art
                      science gateway frameworks.},
      cin          = {JSC},
      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)11},
      url          = {https://juser.fz-juelich.de/record/865715},
}