@inproceedings{M{\"o}hring2023,
  author    = {M{\"o}hring, Michael},
  title     = {Digital twins in production: the integration of semi- and unstructured data},
  booktitle = {Proceedings of the 13th Conference on Learning Factories (CLF 2023), 9-11 May 2023, Reutlingen},
  doi       = {10.2139/ssrn.4469903},
  institution = {Informatik},
  pages     = {1 -- 5},
  year      = {2023},
  abstract  = {Digital twins deployed in production are important in practice and interesting for research. Currently, mostly structured data coming from e.g., sensors and timestamps of related stations, are integrated into Digital Twins. However, semi- and unstructured data are also important to display the current status of a digital twin (e.g., of a machinery or produced good). Process Mining and Text Mining in combination can be used to support the use of log file data to understand the current state of the process as well as highlight issues. Therefore, issue related reactions can be taken more quickly, targeted and cost oriented. Applying a design science research approach; here a prototype as an artefact based on derived requirements is developed. This prototype helps to understand and to clarify the possibilities of Process Mining and Text Mining based on log data for production related Digital Twins. Contributions for practice and research are described. Furthermore, limitations of the research and future opportunities are pointed out.},
  language  = {en}
}