Industrial Data Science

The research field is concerned with the design, analysis, and use of intelligent information systems based on data science methods and machine learning technologies in industrial application domains.

Publications

• Bink R., Zschech P.:
  Predictive Maintenance in der industriellen Praxis: Entwicklung eines Prognoseansatzes unter eingeschränkter Informationslage
  In: HMD : Praxis der Wirtschaftsinformatik 55 (2018), p. 552-565
  ISSN: 1436-3011
  DOI: 10.1365/s40702-017-0378-2
  URL: https://link.springer.com/article/10.1365/s40702-017-0378-2
• Zschech P., Sager C., Siebers P., Pertermann M.:
  Mit Computer Vision zur automatisierten Qualitätssicherung in der industriellen Fertigung: Eine Fallstudie zur Klassifizierung von Fehlern in Solarzellen mittels Elektrolumineszenz-Bildern
  In: HMD : Praxis der Wirtschaftsinformatik (2021), p. 321–342
  ISSN: 1436-3011
  DOI: 10.1365/s40702-020-00641-8
  URL: https://link.springer.com/article/10.1365/s40702-020-00641-8
• Zschech P.:
  A Taxonomy of Recurring Data Analysis Problems in Maintenance Analytics
  26th European Conference on Information Systems (ECIS) (Portsmouth, 23. June 2018 - 28. June 2018)
  In: Proceedings of the 26th European Conference on Information Systems 2018
  URL: https://aisel.aisnet.org/ecis2018_rp/197/
• Zschech P., Bernien J., Heinrich K.:
  Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation
  40th International Conference on Information Systems (ICIS) (München, 15. December 2019 - 18. December 2019)
  In: Proceedings of the 40th International Conference on Information Systems 2019
  URL: https://aisel.aisnet.org/icis2019/data_science/data_science/4/
• Zschech P., Heinrich K., Bink R., Neufeld JS.:
  Prognostic Model Development with Missing Labels: A Condition-Based Maintenance Approach Using Machine Learning
  In: Business & Information Systems Engineering 61 (2019), p. 327-343
  ISSN: 1867-0202
  DOI: 10.1007/s12599-019-00596-1
  URL: https://link.springer.com/article/10.1007/s12599-019-00596-1
• Horn R., Zschech P.:
  Application of Process Mining Techniques to Support Maintenance-Related Objectives
  14th International Conference on Wirtschaftsinformatik (WI) (Siegen, 23. February 2019 - 27. February 2019)
  In: Ludwig T, Pipek V (ed.): Proceedings of the 14th International Conference on Wirtschaftsinformatik, Siegen: 2019
  DOI: 10.25819/ubsi/1016
  URL: https://aisel.aisnet.org/wi2019/specialtrack01/papers/6/
• Zschech P.:
  Data Science and Analytics in Industrial Maintenance: Selection, Evaluation, and Application of Data-Driven Methods (Dissertation, 2020)
  URL: https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-723182
• Wanner J., Heinrich K., Janiesch C., Zschech P.:
  How Much AI Do You Require? Decision Factors for Adopting AI Technology
  41st International Conference on Information Systems (ICIS) (Virtual Conference, 13. December 2020 - 16. December 2020)
  In: Association for Information Systems (ed.): Proceedings of the 41st International Conference on Information Systems 2020
  URL: https://aisel.aisnet.org/icis2020/implement_adopt/implement_adopt/10/
• Janiesch C., Zschech P., Heinrich K.:
  Machine Learning and Deep Learning
  In: Electronic Markets 31 (2021), p. 685–695
  ISSN: 1019-6781
  DOI: 10.1007/s12525-021-00475-2
  URL: https://link.springer.com/article/10.1007/s12525-021-00475-2
• Harl M., Herchenbach M., Kruschel S., Hambauer N., Zschech P., Kraus M.:
  A Light in the Dark: Deep Learning Practices for Industrial Computer Vision
  In: Proceedings of the 17th International Conference on Wirtschaftsinformatik (WI) 2022
  Open Access: https://aisel.aisnet.org/wi2022/student_track/student_track/33/
  URL: https://aisel.aisnet.org/wi2022/student_track/student_track/33/
• Sager C., Zschech P., Kühl N.:
  labelCloud: A Lightweight Labeling Tool for Domain-Agnostic 3D Object Detection in Point Clouds
  In: Computer-Aided Design and Applications 19 (2022), p. 1191-1206
  ISSN: 1686-4360
  DOI: 10.14733/cadaps.2022.1191-1206
  URL: http://cad-journal.net/files/vol_19/CAD_19(6)_2022_1191-1206.pdf