%0 Conference Paper %1 10.1007/978-3-658-21194-3_64 %A Strljic, Matthias Milan %A Tasci, Timur %A Schmidt, Alexander %A Korb, T. %A Riedel, O. %B 18. Internationales Stuttgarter Symposium %C Wiesbaden %D 2018 %E Bargende, Michael %E Reuss, Hans-Christian %E Wiedemann, Jochen %I Springer Fachmedien Wiesbaden %K imported isw %P 843--857 %T A data model for data gathering from heterogeneous IoT and Industry 4.0 applications %X 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. %@ 978-3-658-21194-3

@inproceedings{10.1007/978-3-658-21194-3_64, abstract = {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.}, added-at = {2018-10-08T14:37:26.000+0200}, address = {Wiesbaden}, author = {Strljic, Matthias Milan and Tasci, Timur and Schmidt, Alexander and Korb, T. and Riedel, O.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/26595fb79b9812510ef621c730adcf415/isw-bibliothek}, booktitle = {18. Internationales Stuttgarter Symposium }, editor = {Bargende, Michael and Reuss, Hans-Christian and Wiedemann, Jochen}, interhash = {f7b6322965b26f5642b59fe3cc3c818a}, intrahash = {6595fb79b9812510ef621c730adcf415}, isbn = {978-3-658-21194-3}, keywords = {imported isw}, pages = {843--857}, publisher = {Springer Fachmedien Wiesbaden}, timestamp = {2018-10-11T13:17:29.000+0200}, title = {A data model for data gathering from heterogeneous IoT and Industry 4.0 applications}, year = 2018 }