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@INPROCEEDINGS{Riedel:151081,
      author       = {Riedel, Morris and Memon, Mohammad Shahbaz and Memon, Ahmed
                      and Fiameni, G. and Cacciari, C. and Lippert, Thomas},
      title        = {{H}igh productivity processing - {E}ngaging in big data
                      around distributed computing},
      address      = {Rijeka, Croatia},
      publisher    = {Croatian Society for Information and Communication
                      Technology, Electronics and Microelectronics},
      reportid     = {FZJ-2014-01111},
      pages        = {145-150},
      year         = {2013},
      comment      = {Information $\&$ Communication Technology Electronics $\&$
                      Microelectronics (MIPRO), 2013 36th International Convention
                      on},
      booktitle     = {Information $\&$ Communication
                       Technology Electronics $\&$
                       Microelectronics (MIPRO), 2013 36th
                       International Convention on},
      abstract     = {The steadily increasing amounts of scientific data and the
                      analysis of `big data' is a fundamental characteristic in
                      the context of computational simulations that are based on
                      numerical methods or known physical laws. This represents
                      both an opportunity and challenge on different levels for
                      traditional distributed computing approaches, architectures,
                      and infrastructures. On the lowest level data-intensive
                      computing is a challenge since CPU speed has surpassed IO
                      capabilities of HPC resources and on the higher levels
                      complex cross-disciplinary data sharing is envisioned via
                      data infrastructures in order to engage in the fragmented
                      answers to societal challenges. This paper highlights how
                      these levels share the demand for `high productivity
                      processing' of `big data' including the sharing and analysis
                      of `large-scale science data-sets'. The paper will describe
                      approaches such as the high-level European data
                      infrastructure EUDAT as well as low-level requirements
                      arising from HPC simulations used in distributed computing.
                      The paper aims to address the fact that big data analysis
                      methods such as computational steering and visualization,
                      map-reduce, R, and others are around, but a lot of research
                      and evaluations still need to be done to achieve scientific
                      insights with them in the context of traditional distributed
                      computing infrastructures.},
      month         = {May},
      date          = {2013-05-20},
      organization  = {36th International Convention on
                       Information $\&$ Communication
                       Technology Electronics $\&$
                       Microelectronics, Opatija (Croatia), 20
                       May 2013 - 24 May 2013},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {412 - Grid Technologies and Infrastructures (POF2-412)},
      pid          = {G:(DE-HGF)POF2-412},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/151081},
}