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A data model for data gathering from heterogeneous IoT and Industry 4.0 applications

: Strljic, Matthias M.; Tasci, Timur; Schmidt, Alexander; Korb, Tobias; Riedel, Oliver


Bargende, Michael (Hrsg.):
18. Internationales Stuttgarter Symposium Automobil- und Motorentechnik 2018 : 13. und 14. März 2018, Stuttgart
Berlin: Springer Vieweg, 2018 (Proceedings)
ISBN: 978-3-658-21193-6 (Print)
ISBN: 978-3-658-21194-3 (Online)
Internationales Stuttgarter Symposium Automobil- und Motorentechnik <18, 2018, Stuttgart>
Bundesministerium für Bildung und Forschung BMBF
Innovationen für die Produktion, Dienstleistung und Arbeit von morgen; RetroNet
Retrofitting von Maschinen und Anlagen für die Vernetzung mit Industrie 4.0 Technologie
Fraunhofer IAO ()

Industry 4.0 (I4.0) offers the opportunity to gain a detailed insight into the current production process by means of an increased networking of production plants. This crosslinking makes it possible to record the entire state of a production plant and to trace it within a later analysis. The aim of this analysis is to optimize the monitored production process resulting from analyses of I4.0 value-adding services [1, 2]. Figure 1 schematically visualizes the information flow for such a scenario. Data from the various levels of production are collected, stored in a data storage facility and evaluated by a valueadding service pipeline. The results are integrated back into the production process as optimizations. In this work, first the requirements for such a value-adding service pipeline are determined, which results in a total of five requirements and is abbreviated with R1 to R5. Subsequently, a suitable system architecture from the Big Data area is selected in order to meet the previously established requirements and thus implement a value-adding service pipeline. The requirements R1 - R5 and the system architecture will then flow into a data model for data acquisition and transmission within the shop floor of the production.