Dr. Marwan Hassani is assistant professor at the PA group with a focus on Real-Time Process Mining. His research interests include stream data mining, sequential pattern mining of multiple streams, efficient anytime clustering of big data streams and exploration of evolving graph data. He uses customer journey optimizationa and privacy-aware process mining as use cases for his research. Marwan received his PhD (2015) from RWTH Aachen University where he worked also as a postdoc until July 2016. He coauthored more than 60 scientific publications and serves as organizers for several internationally ranked workshops and multiple A* conferences in data mining.

Position:	UD
Room:	MF 7.068
Tel (internal):	3887
Links:	Courses External assignments Internal assignments Projects Publications
External links:	Personal home page Google scholar page Scopus page ORCID page DBLP page TU/e page

Recent courses

Seminar Process Analytics (2IMI00) 2025-2026 - Objectives This seminar combines teaching research methods (in preparation for a Master project) with providing students with recent and ongoing research in the area of event data analysis and process analysis. We study recent research articles, book chapters, and Master theses on topics along the entire analysis life-cycle. Through presentation and group discussions, we work Read More ...
Advanced Process Mining (2AMI20) 2025-2026 - Objectives After taking this course students should be able to: have a detailed understanding of the entire process mining spectrum and the methodology for process mining analysis

Recent external assignments

Community Detection in Temporal Graphs for Capturing Pass-through Money Laundering Behaviors - Company Description TMNL (Transaction Monitoring Netherlands) was established by the five largest Dutch banks; and is the 1st initiative in the world fighting financial crime in a multi-bank setting. TMNL combines transaction data from the various banks and makes meaningful connections between this data. TMNL is creating smart models to detect these potentially unusual transactions Read More ...
Fine-tuning Pretrained LLMs for Online Anomaly Detection in Customer Journeys of De Volksbank - Background Customer journey thinking is getting more and more established in companies. Customers are getting more volatile with higher expectations and competition is fierce. With customer journey analytics it is possible to use a personal approach on a large dataset of customer behaviour and customer experience. In the field of customer journey analytics multiple well-known Read More ...
Guest Journey Prediction for an Effective Targeted Campaign Planning - Company Description Smart Host (https://www.smart-host.com) was founded in 2017 and is now one of the leading CRM systems for hotels in Europe. Based in Berlin, Germany we provide a SaaS solution to help hotels maximise their revenue and at the same time become better hosts by gathering valuable information about their guests and their individual Read More ...
Online Prediction and Recommendation of Volksbank Customer Journey under Concept Drifts - Background Within companies customer journey thinking is getting more and more established. Customers are getting more volatile and competition is fierce. With customer journey analytics is it possible to use a personal approach on large dataset of customer behaviour and customer experience. In the field of customer journey analytics multiple well-known fields are combined, Read More ...
Applying Process Mining to Predict Customer Behavior and Recommend Actions - Background In the Dutch health care system health care insurance is obligated for all residents. The government sets the basis package and insurers compete based on price and service. Customer service is therefore very important for every health insurance company; especially in the fast changing digital world. As a result customer satisfaction is the most Read More ...
Real-Time Prediction of Traveler Flow within Digital Stations - In the last decennia the pressure on different types of mobility have severely increased in the Netherlands. Therefor the need for availability and reliability has increased. Siemens Mobility supplies solutions in the Netherlands that contribute to the accessibility and quality of life in this regard. With the help of different technologies, data is being unlocked Read More ...
Improving Traffic Flow Prediction in Urban Areas by Incorporating a Real-Time Outlier Detection Model - In the last decennia the pressure on different types of mobility have severely increased in the Netherlands. Therefor the need for availability and reliability has increased. Siemens Mobility supplies solutions in this regard that contribute to everyday accessibility and quality of life. With the help of different technologies, data is being unlocked through which the operation Read More ...
Resolving kpn Customer Journey Variances through a Suitable Similarity Measure - The customer journey approach is quickly becoming the game changer within KPN to become a customer-centric service provider and to improve the customer experience to an un-telco like level. Within this context, we have already connected various touch points of the customer, including calls, chats, store visits, online visits and engineer visits, and we are Read More ...
Enriching Customer Journey Prediction in KPN with Context Data - The customer journey approach is quickly becoming the game changer within KPN to become a customer-centric service provider and to improve the customer experience to an un-telco like level. Within this context, we have already connected various touch points of the customer, including calls, chats, store visits, online visits and engineer visits, and we are Read More ...
Detecting root causes of complaints and investigating the continuation within the customer journey - In the Dutch health care system health care insurance is obligated for all residents. The government sets the basis package and insurers compete based on price and service. Customer service is therefore very important for every health insurance company; especially in the fast changing digital world. As a result customer satisfaction is the most important Read More ...

Recent internal assignments

Log-based vs. Model-based Concept Drift Detection - StrProMCDD is a recently published work that detects concept drifts in event streams (see the figure below). StrProMCDD uses several model-based distance measures to detect these deviations using an adaptive window concept. In this assignment, we would like to compare the performance of this model-based approach with log-based stream clustering approaches that try to detect drifts in Read More ...
Real-Time Process Mining for Customer Journey Data - Available process discovery have been tested in the customer journey context under offline settings. Recent online process discovery approaches like: https://ieeexplore.ieee.org/document/7376771 bring however a lot of added value for a real-time customer journey optimization. The objective of this assignment is to use two different customer journey datasets to test the effectiveness of such approaches for Read More ...
Finding Patterns in Evolving Graphs - The analysis of the temporal evolution of dynamic graphs like social networks is a key challenge for understanding complex processes hidden in graph structured data. Graph evolution rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing rule discovery methods make restrictive assumptions on the Read More ...
Using Sequential Pattern Mining to Detect Drifts in Streaming Data - BFSPMiner is an effective and efficient batch-free algorithm for mining sequential patterns over data streams was published very recently https://link.springer.com/article/10.1007/s41060-017-0084-8. An implementation of the algorithm is available here: https://github.com/Xsea/BFSPMiner. As BFSPMiner has proven to be effective (see Figures 10-14 of the paper) in different domains (see Table 1 in the paper), we would like to Read More ...

Recent projects

Smart Journey Mining: Towards successful digitalisation of services - The digitalisation of our society’s service systems has fundamentally changed the way services are delivered to, and experienced by, humans. Although digital services are supposed to simplify our lives and increase our efficiency, they often frustrate and burden customers, users, and employees. The overall goal is to increase the quality of services and support the Read More ...
BPR4GDPR - Business Process Re-engineering for General Data Protection Regulation Description The goal of BPR4GDPR is to provide a holistic framework able to support end-to-end GDPR-compliant intra- and interorganisational ICT-enabled processes at various scales, while also being generic enough, fulfilling operational requirements covering diverse application domains. To this end, proposed solutions will have a strong semantic foundation Read More ...

Recent publications

Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction - Hassani, M., Verbeek, T., & van Straten, S. (2026). Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction. arXiv.org. https://doi.org/10.48550/arXiv.2604.00653 Abstract Predictive process monitoring (PPM) focuses on predicting future process trajectories, including next activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume Read More ...
Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring - van Straten, S., Padella, A., & Hassani, M. (2026). Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring. In C. Cappiello, O. Hartig, M. Sellami, & A. Ouni (Eds.), Cooperative Information Systems: 31st International Conference, CoopIS 2025, Marbella, Spain, October 20–22, 2025, Proceedings (pp. 70-87). (Lecture Notes in Computer Science (LNCS); Vol. 15535). Springer. https://doi.org/10.1007/978-3-032-15538-2_5 Abstract Read More ...
Task-Free Continual Learning with Dynamic Loss for Online Next Activity Prediction - Verbeek, T., Yao, R., & Hassani, M. (2025). Task-Free Continual Learning with Dynamic Loss for Online Next Activity Prediction. In A. Delgado, & T. Slaats (Eds.), Process Mining Workshops: ICPM 2024 International Workshops, Lyngby, Denmark, October 14–18, 2024, Revised Selected Papers (pp. 693-705). (Lecture Notes in Business Information Processing; Vol. 533). Springer. https://doi.org/10.1007/978-3-031-82225-4_51 Abstract Continual learning, known Read More ...
Sustainable Traffic Flow Prediction using Contrastive Learning and Spatial Encoding - Xu, Y., & Hassani, M. (2025). Sustainable Traffic Flow Prediction using Contrastive Learning and Spatial Encoding. ACM SIGAPP Applied Computing Review, 25(2), 31-46. https://doi.org/10.1145/3746626.3746629 Abstract Urban traffic networks evolve continuously as cities add and relocate sensors, yet most prediction pipelines must be retrained from scratch to exploit these new data sources. Such retraining is costly Read More ...
Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring - van Straten, S., Padella, A., & Hassani, M. (2025). Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring. arXiv.org. https://doi.org/10.48550/arXiv.2507.18293 Abstract Predictive Process Monitoring (PPM) enables forecasting future events or outcomes of ongoing business process instances based on event logs. However, deep learning PPM approaches are often limited by the low variability and small Read More ...
Unsupervised Anomaly Detection of Prefixes in Event Streams Using Online Autoencoders - Musaj, Z., & Hassani, M. (2025). Unsupervised Anomaly Detection of Prefixes in Event Streams Using Online Autoencoders. In M. Comuzzi, D. Grigori, M. Sellami, & Z. Zhou (Eds.), Cooperative Information System: 30th International Conference, CoopIS 2024, Porto, Portugal, November 19–21, 2024, Proceedings (pp. 93-110). Springer. https://doi.org/10.1007/978-3-031-81375-7_6 Abstract In this work we address the problem of Read More ...
Autoencoder-Based Detection of Delays, Handovers and Workloads over High-Level Events - Verwijst, I., Mennens, R., Scheepens, R., & Hassani, M. (2025). Autoencoder-Based Detection of Delays, Handovers and Workloads over High-Level Events. In M. Comuzzi, D. Grigori, M. Sellami, & Z. Zhou (Eds.), Cooperative Information Systems: 30th International Conference, CoopIS 2024, Porto, Portugal, November 19–21, 2024, Proceedings (pp. 111-128). (Lecture Notes in Computer Science (LNCS); Vol. 15506). Read More ...
Handling Catastrophic Forgetting: Online Continual Learning for Next Activity Prediction - Verbeek, T., & Hassani, M. (2025). Handling Catastrophic Forgetting: Online Continual Learning for Next Activity Prediction. In M. Comuzzi, D. Grigori, M. Sellami, & Z. Zhou (Eds.), Cooperative Information Systems – 30th International Conference, CoopIS 2024, Proceedings: 30th International Conference, CoopIS 2024, Porto, Portugal, November 19–21, 2024, Proceedings (pp. 225-242). (Lecture Notes in Computer Science Read More ...
Outlier-Weighted Traffic Flow Prediction Using Online Autoencoders - Choudhary, H., Alkhodre, A. B., & Hassani, M. (2025). Outlier-Weighted Traffic Flow Prediction Using Online Autoencoders. In R. Chbeir, S. Ilarri, Y. Manolopoulos, P. Z. Revesz, J. Bernardino, & C. K. Leung (Eds.), Database Engineered Applications: 28th International Symposium, IDEAS 2024, Bayonne, France, August 26–29, 2024, Proceedings (pp. 203-219). (Lecture Notes in Computer Science (LNCS); Read More ...
Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions - Tariq, H., & Hassani, M. (2025). Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions. In R. Meo, & F. Silvestri (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers, Part V (pp. 402-419). (Communications in Computer Read More ...

Recent awards

Best Paper Award at SAC 2025 for You Xu and Marwan Hassani - You Xu and Marwan Hassani have received the Best Paper Award Distributed Systems in the ACM/SIGAPP Symposium On Applied Computing (SAC 2025) in Catania, Italy with the paper titled “Leveraging Contrastive Learning and Spatial Encoding for Prediction in Traffic Networks with Expanding Infrastructure“. Congratulations to You and Marwan!