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@INPROCEEDINGS{Lippert:283753,
      author       = {Lippert, Thomas and Mallmann, Daniel and Riedel, Morris},
      title        = {{S}cientific {B}ig {D}ata {A}nalytics by {HPC}},
      volume       = {48},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH, Zentralbibliothek},
      reportid     = {FZJ-2016-02040},
      series       = {NIC Series},
      pages        = {1-10},
      year         = {2016},
      comment      = {NIC Symposium 2016},
      booktitle     = {NIC Symposium 2016},
      abstract     = {Storing, managing, sharing, curating and especially
                      analysing huge amounts of data face an immense visibility
                      and importance in industry and economy as well as in science
                      and research. Industry and economy exploit “Big Data”
                      for predictive analysis, to increase the efficiency of
                      infrastructures, customer segmentation, and tailored
                      services. In science, Big Data allows for addressing
                      problems with complexities that were impossible to deal with
                      so far. The amounts of data are growing exponentially in
                      many areas and are becoming a drastical challenge for
                      infrastructures, software systems, analysis methods, and
                      support structures, as well as for funding agencies and
                      legislation.In this contribution, we argue that the
                      Helmholtz Association, with its objective to build and
                      operate large-scale experiments, facilities, and research
                      infrastructures, has a key role in tackling the pressing
                      Scientific Big Data Analytics challenge. DataLabs and
                      SimLabs, sustained on a long-term basis in Helmholtz, can
                      bring research groups together on a synergistic level and
                      can transcend the boundaries between different communities.
                      This allows to translate methods and tools between different
                      domains as well as from fundamental research to applications
                      and industry. We present an SBDA framework concept touching
                      its infrastructure building blocks, the targeted user groups
                      and expected benefits, also concerning industry aspects.
                      Finally, we give a preliminary account on the call for
                      “Expressions of Interest” by the John von
                      Neumann-Institute for Computing concerning Scientific Big
                      Data Analytics by HPC.},
      month         = {Feb},
      date          = {2016-02-11},
      organization  = {NIC Symposium 2016, Jülich (Germany),
                       11 Feb 2016 - 12 Feb 2016},
      cin          = {JSC / NIC},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)NIC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512)},
      pid          = {G:(DE-HGF)POF3-512},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://juser.fz-juelich.de/record/283753},
}