Trustworthy AI for Suffix Prediction of Business Processes

Provided and advised by: Henryk Mustroph
Predictive Process Monitoring (PPM) uses historical event logs to forecast how an ongoing case (referred to as a prefix) will continue or finish. PPM comprises four main subtopics: 1) Outcome Prediction: For example, in a loan application process, predicting whether the loan will be approved, or the process will be cancelled before completion. 2) Remaining Time Prediction: Estimating the remaining time until the process completion. 3) Next Activity Prediction: Forecasting the next activity that will occur in the process. 4) Suffix Prediction: Aiming to predict the sequence of remaining events (the suffix) until the process is completed. Modern approaches to PPM, particularly Suffix Prediction, increasingly rely on Deep Neural Networks (NNs).

Thesis Topics:

1) Explainable Suffix Prediction of Business Processes (Master)

NNs are inherently black boxes, meaning they lack interpretability and transparency. Especially, in high-risk domains interpretability and transparency are important to generate user trust. Explainable AI (XAI) seeks to bridge the gap in transparency by providing human-understandable justifications for the outputs of NNs. There are two main approaches to implementing XAI in deep neural networks: 1) Intrinsic Explainability: Designing the NN to be inherently more interpretable, for example by incorporating attention mechanisms that indicate which parts of the input were most influential in making a prediction. 2) Post-hoc Explainability: Applying interpretability techniques after the NN has been trained, such as feature importance scores or attribution methods. In this thesis, XAI techniques will be explored and applied to a NN model for suffix prediction, with a prototypical implementation, and a quantitative/ qualitative evaluation and comparison to current state-of-the-art approaches. Further details will be shared in a meeting after a successful application.

2) Robust Suffix Prediction of Business Processes (Master)

In practice, event logs often contain noise due to human involvement in the process and high variability in process execution. This noise can stem from factors such as for example inconsistencies in data recording. As a result, event logs may include missing or mislabeled event attributes, irregular timestamps, or over- or under-representation of certain traces. Robustness in this context refers to building NNs that not only perform well on clean data but also provide reliable predictions when faced with unknown or underrepresented prefixes at test time or at least signal their uncertainty when predictions may be unreliable. To achieve this, several techniques can be employed, such as: Data-centric augmentation, to simulate missing events. Regularization strategies, which penalize the NN’s sensitivity to small input perturbations. Adversarial training, which improves the NN’s resilience to subtle variations and edge cases. These approaches enhance the NN’s reliability, and performance, especially for unseen traces in real-world applications and increases user trust in the predictions. In this thesis, robustness techniques will be explored and applied to a deep neural network model for suffix prediction, with a prototypical implementation, and a quantitative evaluation and comparison to current state-of-the-art approaches. Further details will be shared in a meeting after a successful application.

Recommended Readings:
- Stevens, A., & De Smedt, J. (2024). Explainability in process outcome prediction: Guidelines to obtain interpretable and faithful models. European Journal of
Operational Research, 317(2), 317-329.
- Stevens, A., De Smedt, J., Peeperkorn, J., & De Weerdt, J. (2022, October). Assessing the robustness in predictive process monitoring through adversarial
attacks. In 2022 4th International Conference on Process Mining (ICPM) (pp. 56-63). IEEE.
- Mustroph, H., Kunkler, M., & Rinderle-Ma, S. (2025). An Uncertainty-Aware ED-LSTM for Probabilistic Suffix Prediction. arXiv preprint arXiv:2505.21339.
- Mehdiyev, N., Majlatow, M., & Fettke, P. (2023). Explainable Artificial Intelligence Meets Uncertainty Quantification for Predictive Process Monitoring. In PMAI@IJCAI (pp. 29-32).
The application must contain:
- Thesis topic you are interested in.
- Current Transcript of Records.
- Application form
- CV
Please send the application to master.i17(at)in.tum.de AND (in cc) henryk.mustroph(at)tum.de.