2020

• Brunk J., Stottmeister J., Weinzierl S., Matzner M., Becker J.:
  Exploring the effect of context information on deep learning business process predictions
  In: Journal of Decision Systems (2020), p. 1-16
  ISSN: 1246-0125
  DOI: 10.1080/12460125.2020.1790183
• Harl M., Weinzierl S., Stierle M., Matzner M.:
  Explainable predictive business process monitoring using gated graph neural networks
  In: Journal of Decision Systems (2020), p. 1-16
  ISSN: 1246-0125
  DOI: 10.1080/12460125.2020.1780780
• Marx E., Stierle M., Weinzierl S., Matzner M.:
  Closing the Gap between Smart Manufacturing Applications and Data Management
  15th International Conference on Wirtschaftsinformatik (Potsdam)
  DOI: 10.30844/wi_2020_u1-marx
  URL: https://library.gito.de/open-access-pdf/U1_Marx-Closing_the_Gap_between_Smart_Manufacturing_Applications_and_Data_Management-525_c.pdf
• Nguyen A., Chatterjee S., Weinzierl S., Schwinn L., Matzner M., Eskofier B.:
  Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
  International Conference on Process Mining (Padua, 4. October 2020 - 9. October 2020)
  In: Proceedings of the 1st International Workshop on Leveraging Machine Learning in Process Mining 2020
• Weinzierl S., Dunzer S., Zilker S., Matzner M.:
  Prescriptive Business Process Monitoring for Recommending Next Best Actions
  International Conference on Business Process Management (Sevilla, 13. September 2020 - 18. September 2020)
  In: Proceedings of the 18th International Conference on Business Process Management Forum 2020
  DOI: 10.1007/978-3-030-58638-6_12
  URL: https://link.springer.com/content/pdf/10.1007/978-3-030-58638-6_12.pdf
• Weinzierl S., Stierle M., Zilker S., Matzner M.:
  A next click recommender system for web-based service analytics with context-aware LSTMs
  Hawaii International Conference on System Sciences (Grand Wailea, Maui, Hawaii, 7. January 2020 - 10. January 2020)
  In: Proceedings of the 53rd Hawaii International Conference on System Sciences 2020
  DOI: 10.24251/HICSS.2020.190
  URL: http://hdl.handle.net/10125/63929
• Weinzierl S., Wolf V., Pauli T., Beverungen D., Matzner M.:
  Detecting Workarounds in Business Processes — A Deep Learning Method for Analyzing Event Logs
  European Conference on Information Systems (Marrakesch, 15. June 2020 - 17. June 2020)
  In: Proceedings of the 28th European Conference on Information Systems 2020
  URL: https://www.researchgate.net/publication/341180737_DETECTING_WORKAROUNDS_IN_BUSINESS_PROCESSES_-_A_DEEP_LEARNING_METHOD_FOR_ANALYZING_EVENT_LOGS
• Weinzierl S., Zilker S., Brunk J., Revoredo K., Matzner M., Becker J.:
  XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
  International Conference on Business Process Management (Sevilla, 13. September 2020 - 18. September 2020)
  In: Proceedings of the 4th International Workshop on Artificial Intelligence for Business Process Management 2020
  DOI: 10.1007/978-3-030-66498-5_10
• Weinzierl S., Zilker S., Stierle M., Park G., Matzner M.:
  From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events
  Internationale Tagung Wirtschaftsinformatik (Potsdam, 8. March 2020 - 11. March 2020)
  In: Proceedings of the 15th International Conference on Wirtschaftsinformatik 2020
  DOI: 10.30844/wi_2020_c12-weinzierl
  URL: https://library.gito.de/open-access-pdf/C12_Prescriptive_process_monitoring_-_a_technique_for_determining_next_best_actions_resub.pdf

2019

• Weinzierl S., Revoredo K., Matzner M.:
  Predictive Businss Process Monitoring With Context Information From Documents
  European Conference on Information Systems (Stockholm, 8. June 2019 - 14. June 2019)
  In: Proceedings of the 27th European Conference on Information Systems 2019
  URL: https://www.researchgate.net/publication/333245929_PREDICTIVE_BUSINESS_PROCESS_MONITORING_WITH_CONTEXT_INFORMATION_FROM_DOCUMENTS