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000173108 0247_ $$2doi$$a10.1109/MIPRO.2014.6859579
000173108 037__ $$aFZJ-2014-06521
000173108 1001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b0$$eCorresponding Author$$ufzj
000173108 1112_ $$a2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)$$cOpatija$$d2014-05-26 - 2014-05-30$$wCroatia
000173108 245__ $$aHigh productivity data processing analytics methods with applications
000173108 260__ $$bIEEE$$c2014
000173108 29510 $$a2014 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
000173108 300__ $$a289 - 294
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000173108 520__ $$aThe 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.
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000173108 7001_ $$0P:(DE-Juel1)132190$$aMemon, Mohammad Shahbaz$$b1$$ufzj
000173108 7001_ $$0P:(DE-Juel1)132191$$aMemon, Ahmed$$b2$$ufzj
000173108 773__ $$a10.1109/MIPRO.2014.6859579
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000173108 9132_ $$0G:(DE-HGF)POF3-512$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data $$vData-Intensive Science and Federated Computing$$x0
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000173108 9141_ $$y2014
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