SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data. Grüger, J., Malburg, L., Mangler, J., Bertrand, Y., Rinderle-Ma, S., Bergmann, R., & Serral Asensio, E. CoRR, 2022.
SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data [link]Paper  abstract   bibtex   
Process management and process orchestration/execution are currently hot topics; prevalent trends such as automation and Industry 4.0 require solutions which allow domain-experts to easily model and execute processes in various domains, including manufacturing and health-care. These domains, in turn, rely on a tight integration between hardware and software, i.e. via the Internet of Things (IoT). While process execution is about actuation, i.e. actively triggering actions and awaiting their completion, accompanying IoT sensors monitor humans and the environment. These sensors produce large amounts of procedural, discrete, and continuous data streams, that hold the key to understanding the quality of process subjects (e.g. produced parts), outcome (e.g. quantity and quality), and error causes. Processes constantly evolve in conjunction with their IoT environment. This requires joint storage of data generated by processes, with data generated by the IoT sensors is therefore needed. In this paper, we present an extension of the process log standard format XES, namely SensorStream. SensorStream enables to connect IoT data to process events, as well as a set of semantic annotations to describe the scenario and environment during data collection. This allows to preserve the full context required for data-analysis, so that logs can be analyzed even when scenarios or hardware artifacts are rapidly changing. Through additional semantic annotations, we envision the XES extension log format to be a solid based for the creation of a (semi-)automatic analysis pipeline, which can support domain experts by automatically providing data visualization, or even process insights.
@article{Grueger.2022_SensorStreamExtension,
  author    = {Joscha Grüger and Lukas Malburg and Jürgen Mangler and Yannis Bertrand and Stefanie Rinderle-Ma and Ralph Bergmann and Estefanía {Serral Asensio}},
  title     = {{SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data}},
  journal   = {CoRR},
  volume    = {abs/2206.11392},
  year      = {2022},
  url       = {https://arxiv.org/abs/2206.11392},
  archivePrefix = {arXiv},
  eprint    = {2206.11392},
  abstract = {Process management and process orchestration/execution are currently hot topics; prevalent trends such as automation and Industry 4.0 require solutions which allow domain-experts to easily model and execute processes in various domains, including manufacturing and health-care. These domains, in turn, rely on a tight integration between hardware and software, i.e. via the Internet of Things (IoT). While process execution is about actuation, i.e. actively triggering actions and awaiting their completion, accompanying IoT sensors monitor humans and the environment. These sensors produce large amounts of procedural, discrete, and continuous data streams, that hold the key to understanding the quality of process subjects (e.g. produced parts), outcome (e.g. quantity and quality), and error causes. Processes constantly evolve in conjunction with their IoT environment. This requires joint storage of data generated by processes, with data generated by the IoT sensors is therefore needed. In this paper, we present an extension of the process log standard format XES, namely SensorStream. SensorStream enables to connect IoT data to process events, as well as a set of semantic annotations to describe the scenario and environment during data collection. This allows to preserve the full context required for data-analysis, so that logs can be analyzed even when scenarios or hardware artifacts are rapidly changing. Through additional semantic annotations, we envision the XES extension log format to be a solid based for the creation of a (semi-)automatic analysis pipeline, which can support domain experts by automatically providing data visualization, or even process insights.}
}

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