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Dataset . 2023
License: CC BY
Data sources: Datacite
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Dataset . 2023
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Data set from Fischertechnik Smart Factory Model at University of St.Gallen (Custom Python Configuration)

Authors: Seiger, Ronny;

Data set from Fischertechnik Smart Factory Model at University of St.Gallen (Custom Python Configuration)

Abstract

This is about 60 mins worth of data collected from Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen. In this data set, we used a custom Python-based software stack to control the smart factory via a business process system (Camunda Platform) that calls the functionality of the smart factory via web services implemented in Python flask. MQTT is used to collect the data. Each entry in the file (low-level_log_20230206-140808.txt) corresponds to one message (as JSON object) received on a specific topic via MQTT. Each line contains all the readings of all the sensors, actuators and additional data from one CPS component (i.e., production station) at one point in time. The data set contains the following files low-level_log_20230206-140808.txt: low-level IoT data from all the sensors and actuators *.bpmn: executable BPMN 2.0 models of three different processes that have been executed several times via the Camunda Platform BPM system to control the smart factory camunda_process-instance.json: event log generated by the BPM system regarding the process instance execution camunda_activity-instance.json: event log generated by the BPM system regarding the activity instance execution Check the following publications to learn more about our research using the model factory: Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18 (pp. 95-107). Springer International Publishing. Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In 2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 20-26). IEEE. Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. Journal of Manufacturing Systems, 63, 575-592.

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Keywords

smart factory, industry 4.0, internet of things

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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