Marwan Hassani

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
Honors
Internal assignments
Projects
Publications
External links: Personal home page
Google scholar page
Scopus page
ORCID page
DBLP page
TU/e page

Recent courses

  • Real-Time Process Mining (JM0210-M-6) 2023 - The course starts with an overview of the BPM domain using a set of twenty BPM use cases. These cover four key BPM activities: model (creating a process model to be used for analysis or enactment), enact (using a process model to control and support concrete cases), analyze (analyzing a process using a process model Read More ...
  • Seminar Process Analytics (2IMI00) 2023 - This seminar prepares students for their Master project. By studying recent literature, we discuss and identify how to develop a research question, select the right research method, and plan and conduct the right evaluation. In addition, students get in touch with recent and ongoing research and practical application in the area of Processes and Information Read More ...
  • Advanced Process Mining (2AMI20) 2023 - Many real-life phenomena studied with Data Science methods unfold over time. They often involve many people, objects, agents, machines, entities, etc. that interact with each other while distributed in time and space. Such dynamics are called processes and are present everywhere: in software systems medical treatments, logistics systems, manufacturing, and even entire organizations. Process mining Read More ...

Recent external assignments

Recent honors

  • HA700 Honors part 1 track Big Data - Big Data In today’s connected world, data is everywhere and everybody talks about the analysis of “big data”. But what is this big data exactly and what can you do with it? In this track, you will learn about the state-of-the-art in data analysis through a number of company visits and literature. You will experience that Read More ...

Recent internal assignments

  • Online Spatial Prediction Model for Citizens’ Public Space Complaints in Eindhoven -   Smart cities approach does not only emphasize the implementations of new technologies in a city but also highlights the importance of using new technologies for enabling citizens’ engagement in urban planning processes. In that regard, ICTs play a vital role in (i) supporting citizens to report their complaints related to the public spaces (i.e. Read More ...
  • 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

  • Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems - Mertens, T., & Hassani, M. (2023). Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems. In M.-R. Amini, S. Canu, A. Fischer, T. Guns, P. Kralj Novak, & G. Tsoumakas (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part VI Read More ...
  • Conformance checking of process event streams with constraints on data retention - Zaman, R., Hassani, M., & van Dongen, B. F. (2023). Conformance checking of process event streams with constraints on data retention. Information Systems, 117, Article 102228. https://doi.org/10.1016/j.is.2023.102228 Abstract Conformance checking (CC) techniques in process mining determine the conformity of cases, by means of their event sequences, with respect to a business process model. Online conformance Read More ...
  • Predicting Activities of Interest in the Remainder of Customer Journeys Under Online Settings - Wolters, L., & Hassani, M. (2023). Predicting Activities of Interest in the Remainder of Customer Journeys Under Online Settings. In M. Montali, A. Senderovich, & M. Weidlich (Eds.), Process Mining Workshops – ICPM 2022 International Workshops, Revised Selected Papers (pp. 145-157). (Lecture Notes in Business Information Processing; Vol. 468 LNBIP). https://doi.org/10.1007/978-3-031-27815-0_11 Abstract Customer journey analysis Read More ...
  • PrefixCDD: Effective Online Concept Drift Detection over Event Streams using Prefix Trees. - Huete, J., Qahtan, A. A., & Hassani, M. (2023). PrefixCDD: Effective Online Concept Drift Detection over Event Streams using Prefix Trees. In H. Shahriar, Y. Teranishi, A. Cuzzocrea, M. Sharmin, D. Towey, AKM. J. A. Majumder, H. Kashiwazaki, J.-J. Yang, M. Takemoto, N. Sakib, R. Banno, & S. I. Ahamed (Eds.), COMPSAC (pp. 328-333) https://doi.org/10.1109/COMPSAC57700.2023.00051 Read More ...
  • Evaluation of Probability Distribution Distance Metrics in Traffic Flow Outlier Detection - Andersen, E., Chiarandini, M., Hassani, M., Janicke, S., Tampakis, P., & Zimek, A. (2022). Evaluation of Probability Distribution Distance Metrics in Traffic Flow Outlier Detection. In Proceedings – 2022 23rd IEEE International Conference on Mobile Data Management, MDM 2022 (pp. 64-69). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/MDM55031.2022.00030 Abstract Recent approaches have proven the effectiveness Read More ...
  • Clustering-based Aggregations for Prediction in Event Streams - Spenrath, Y., Hassani, M., & Dongen, B. F. V. (2022). Clustering-based Aggregations for Prediction in Event Streams. CoRR, abs/2210.09738. https://doi.org/10.48550/arXiv.2210.09738
  • Online Prediction of Aggregated Retailer Consumer Behaviour - Spenrath, Y., Hassani, M., & van Dongen, B. F. (2022). Online Prediction of Aggregated Retailer Consumer Behaviour. In J. Munoz-Gama, & X. Lu (Eds.), Process Mining Workshops – ICPM 2021 International Workshops, Revised Selected Papers (pp. 211-223). (Lecture Notes in Business Information Processing; Vol. 433 LNBIP). Springer. https://doi.org/10.1007/978-3-030-98581-3_16 Abstract Predicting the behaviour of consumers provides valuable Read More ...
  • BitBooster: Effective Approximation of Distance Metrics via Binary Operations - Spenrath, Y., Hassani, M., & Van Dongen, B. F. (2022). BitBooster: Effective Approximation of Distance Metrics via Binary Operations. In H. Va Leong, S. S. Sarvestani, Y. Teranishi, A. Cuzzocrea, H. Kashiwazaki, D. Towey, J-J. Yang, & H. Shahriar (Eds.), Proceedings – 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022 (pp. 201-210). Read More ...
  • Efficient Memory Utilization in Conformance Checking of Process Event Streams - Zaman, R., Hassani, M., & van Dongen, B. F. (2022). Efficient Memory Utilization in Conformance Checking of Process Event Streams. 437-440. https://doi.org/10.1145/3477314.3507217 Abstract Conformance checking (CC) techniques of the process mining field gauge the conformance of the events constituting a case with respect to a business process model. Online conformance checking (OCC) techniques assess such Read More ...
  • A Framework for Efficient Memory Utilization in Online Conformance Checking - Zaman, R., Hassani, M., & van Dongen, B. F. (2021). A Framework for Efficient Memory Utilization in Online Conformance Checking. arXiv.org. https://arxiv.org/pdf/2112.13640.pdf Abstract Conformance checking (CC) techniques of the process mining field gauge the conformance of the sequence of events in a case with respect to a business process model, which simply put is an Read More ...

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