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Process-Aware Prediction from Event Logs using Machine Learning
Ivanyi, Zsombor (2025) Process-Aware Prediction from Event Logs using Machine Learning.
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| Abstract: | The increasing availability of detailed event logs from industrial and service processes offer new opportunities to support operational decision-making through predictive analytics. This work aims to investigate how predictive models trained on historical event logs perform under changing process conditions, such as disruptions, and to assess their generalization in logistics environments. An empirical study based on a logistics and manufacturing dataset is conducted, evaluating the accuracy and robustness of Long Short-Term Memory (LSTM) models in different scenarios, including process disruptions. The findings highlight not only which ML models perform best under various conditions, but also how predictive values can potentially augment logs to support proactive decision making in operational environments such as logistics and healthcare. This has the potential to enable downstream use in process discovery, conformance checking, and process enhancement. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/107573 |
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