Big data analytics for proactive industrial decision support
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Artikelnummer
03654_2016_09_04
Approaches and first experiences in the FEE Project
Big data technologies offer new opportunities for analyzing historical data generated by process plants. The development of new types of operator support systems (OSS) which help the plant operators during operations and in dealing with critical situations is one of these possibilities. The project FEE has the objective to develop such support functions based on big data analytics of historical plant data. In this contribution, we share our first insights and lessons learned in the development of big data applications and outline the approaches and tools that we developed in the course of the project.
| Autoren | Martin Atzmueller/University Of Kassel / Benjamin Klöpper/Abb Corporate Research Center Germany / Hassan Al Mawla/Abb Corporate Research Center Germany / Benjamin Jäschke/University Of Kassel / Martin Hollender/Abb Corporate Research Center Germany / Markus Graube/Technische Universität Dresden / David Arnu/Rapidminer / Andreas Schmidt/University Of Kassel / Sebastian Heinze/Technische Universität Dresden / Lukas Schorer/Abb Corporate Research Center Germany / Andreas Kroll/University Of Kassel / Gerd Stu |
|---|---|
| Erscheinungsdatum | 01.09.2016 |
| Format | |
| Zeitschrift | atp edition - Ausgabe 09 2016 |
| Verlag | DIV Deutscher Industrieverlag GmbH |
| Titel | Big data analytics for proactive industrial decision support |
| Untertitel | Approaches and first experiences in the FEE Project |
| Beschreibung | Big data technologies offer new opportunities for analyzing historical data generated by process plants. The development of new types of operator support systems (OSS) which help the plant operators during operations and in dealing with critical situations is one of these possibilities. The project FEE has the objective to develop such support functions based on big data analytics of historical plant data. In this contribution, we share our first insights and lessons learned in the development of big data applications and outline the approaches and tools that we developed in the course of the project. |
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