Research Seminar on AI: Explainable AI in practice: Remaining Time Prediction for Processes with Inter-Case Dynamics

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Mahsa Pourbafrani


Mahsa Pourbafrani


+49 241 80 21906



Tuesday, December 14, 2021, 4:00pm

Process mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. Mahsa Pourbafrani and her Team propose an approach that decreases the previous approaches’ reliance on user knowledge for discovering fine-grained process behavior. Furthermore, Mahsa Pourbafrani and her Team enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. Mahsa Pourbafrani and her Team assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.

Mahsa Pourbafrani is a research assistant in the Data and Process Science group of RWTH Aachen University. She is a Ph.D. student under Professor van der Aalst's supervision. Her research focuses on forward-looking process mining, which employs data science methods to turn data into actionable insights. The actions are taken using simulation, what-if analysis, and predictions in process mining regarding the performance metrics of processes. She is also a scientist working on the "Internet of Production" project, which aims to combine process mining and machine learning techniques to support operations and decisions in production lines.


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