Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Dataset from a Smart Factory to evaluate a Semi-automated Approach to Detecting Process-Level Activities from Sensor Data

Authors: Seiger, Ronny;

Dataset from a Smart Factory to evaluate a Semi-automated Approach to Detecting Process-Level Activities from Sensor Data

Abstract

This dataset contains sensor and process data related to the execution of storage and production processes collected from a Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen. This dataset is used in the evaluation of the paper "A Semi-automated Approach to Detecting Process-level Activities from Sensor Data", authored by Luciano García-Bañuelos, Mauricio Jacobo González González, Ronny Seiger, Marco Franceschetti and Alejandra Guadalupe Silva Trujillo, published in the proceedings of the 8th International Conference on Emerging Data and Industry (EDI40) (DOI: 10.1016/j.procs.2025.03.110). In this dataset, 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. The sensor data from the smart factory is collected at a frequency of 10 Hz. The data set contains the following files with low-level IoT (sensor) data: training_tenhertz_log_20230411-095748.txt: contains the training data comprising 5 executions of each, the storage and production process. test_tenhertz_log_20230411-103455.txt: contains a larger dataset for testing with several executions of the storage and production processes. Each entry in these files corresponds to one message (as JSON object). 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. Moreover, the dataset contains the following process (BPM)-related files: storage_process.bpmn: executable BPMN 2.0 model of the storage process that has been executed several times via the Camunda Platform BPM system to control the smart factory production_process.bpmn: executable BPMN 2.0 model of the production process that has 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 (ground truth) camunda_activity-instance.json: event log generated by the BPM system regarding the activity instance execution (ground truth)

Keywords

Activity Detection, Industry 4.0, Smart Factory, Business Process Management

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    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).
    0
    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.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average