@article{Grger2023,
  doi = {10.1515/itit-2022-0077},
  url = {https://doi.org/10.1515/itit-2022-0077},
  year = {2023},
  volume = {65},
  number = {3},
  month = jun,
  publisher = {Walter de Gruyter {GmbH}},
  author = {Joscha Gr\"{u}ger and Lukas Malburg and Ralph Bergmann},
  title = {{IoT}-enriched event log generation and quality analytics: a case study},
  url          = {http://www.wi2.uni-trier.de/shared/publications/10.1515_itit-2022-0077.pdf},
  journal = {it - Information Technology},
  keywords     = {{Data Quality in Event Logs, DataStream XES Extension, IoT, IoT-Enriched Event Log, Physical Smart Factory, Process Mining}},
  abstract     = {Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various fields, including business process management (BPM) research. An important area of research in BPM is process mining, which can be used to analyze event logs e.g., to check the conformance of running processes. However, the data ingested in IoT environments often contain data quality issues (DQIs) due to system complexity and sensor heterogeneity, among other factors. To date, however, there has been little work on IoT event logs, DQIs occurring in them, and how to handle them. In this case study, we generate an IoT event log, perform a structured data quality analysis, and describe how we addressed the problems we encountered in pre-processing.}
}

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