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001     173108
005     20210129214552.0
024 7 _ |a 10.1109/MIPRO.2014.6859579
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037 _ _ |a FZJ-2014-06521
100 1 _ |a Riedel, Morris
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111 2 _ |a 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
|c Opatija
|d 2014-05-26 - 2014-05-30
|w Croatia
245 _ _ |a High productivity data processing analytics methods with applications
260 _ _ |c 2014
|b IEEE
295 1 0 |a 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
300 _ _ |a 289 - 294
336 7 _ |a Contribution to a conference proceedings
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336 7 _ |a Contribution to a book
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336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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520 _ _ |a 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.
536 _ _ |a 412 - Grid Technologies and Infrastructures (POF2-412)
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588 _ _ |a Dataset connected to CrossRef Conference
700 1 _ |a Memon, Mohammad Shahbaz
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700 1 _ |a Memon, Ahmed
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773 _ _ |a 10.1109/MIPRO.2014.6859579
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910 1 _ |a Forschungszentrum Jülich GmbH
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913 2 _ |a DE-HGF
|b Key Technologies
|l Supercomputing & Big Data
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|v Data-Intensive Science and Federated Computing
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913 1 _ |a DE-HGF
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914 1 _ |y 2014
920 _ _ |l yes
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