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@INPROCEEDINGS{Riedel:173108,
      author       = {Riedel, Morris and Memon, Mohammad Shahbaz and Memon,
                      Ahmed},
      title        = {{H}igh productivity data processing analytics methods with
                      applications},
      publisher    = {IEEE},
      reportid     = {FZJ-2014-06521},
      pages        = {289 - 294},
      year         = {2014},
      comment      = {2014 37th International Convention on Information and
                      Communication Technology, Electronics and Microelectronics
                      (MIPRO) : [Proceedings] - IEEE, 2014. - ISBN
                      978-953-233-077-9978-953-233-081-6 -
                      doi:10.1109/MIPRO.2014.6859579},
      booktitle     = {2014 37th International Convention on
                       Information and Communication
                       Technology, Electronics and
                       Microelectronics (MIPRO) :
                       [Proceedings] - IEEE, 2014. - ISBN
                       978-953-233-077-9978-953-233-081-6 -
                       doi:10.1109/MIPRO.2014.6859579},
      abstract     = {The term `big data analytics' emerged in order to engage in
                      the ever increasing amount of scientific and engineering
                      data with general analytics techniques that support the
                      often more domain-specific data analysis process. It is
                      recognized that the big data challenge can only be
                      adequately addressed when knowledge of various different
                      fields such as data mining, machine learning algorithms,
                      parallel processing, and data management practices are
                      effectively combined. This paper thus describes some of the
                      `smart data analytics methods' that enable a high
                      productivity data processing of large quantities of
                      scientific data in order to enhance the data analysis
                      efficiency. The paper thus aims to provide new insights how
                      various fields can be successfully combined. Contributions
                      of this paper include the concretization of the
                      cross-industry standard process for data mining (CRISP-DM)
                      process model in scientific environments using concrete
                      machine learning algorithms (e.g. support vector machines
                      that enable data classification) or data mining mechanisms
                      (e.g. outlier detection in measurements). Serial and
                      parallel approaches to specific data analysis challenges are
                      discussed in the context of concrete earth science
                      application data sets. Solutions also include various data
                      visualizations that enable a better insight in the
                      corresponding data analytics and analysis process.},
      month         = {May},
      date          = {2014-05-26},
      organization  = {2014 37th International Convention on
                       Information and Communication
                       Technology, Electronics and
                       Microelectronics (MIPRO), Opatija
                       (Croatia), 26 May 2014 - 30 May 2014},
      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},
      doi          = {10.1109/MIPRO.2014.6859579},
      url          = {https://juser.fz-juelich.de/record/173108},
}