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Personal profile

Quote

"Unsupervised online learning from event streams is essential for applications with limited domain knowledge and real time demands." 

Research profile

Marwan Hassani is leading a small research group on streaming process mining within the Process Analytic cluster. Marwan focuses in his research on online knowledge extraction from streaming event data and its applications in stream process mining. Marwan’s main interests are developing and applying unsupervised learning methods such as clustering, outlier detection and sequential pattern mining over streaming data. His main use case is optimizing customer journey and similar applications where scalable solutions are needed in an online manner and with limited availability of domain knowledge.

Academic background

Marwan Hassani has received a PhD in Computer Science in 2015 from RWTH Aachen University in Germany where he was a PhD candidate under the supervision of Thomas Seidl in the Data Management and Data Exploration Group. During his PhD, Marwan visited the School of Computing at Portsmouth University, UK. Marwan acted as a postdoctoral researcher at RWTH Aachen University from 2015 till July 2016. Since August 2016 he is an assistant professor in computer science at Eindhoven University of Technology TU/e where he is also leading the Customer Journey track of the Data Science Center Eindhoven (DSC/e). He has published over 65 scientific papers on Data Mining and Process Mining. Marwan has served in the program committees of major conferences and journals related to data mining, including ECML/PKDD, SDM, CIKM, KAIS, DAMI, JMLR. He has also co-chaired numerous scientific events on various data mining and process mining topics.  

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Projects

Research Output

Facilitating GDPR compliance: the H2020 BPR4GDPR approach

Lioudakis, G. V., Koukovini, M. N., Papagiannakopoulou, E. I., Dellas, N., Kalaboukas, K., de Carvalho, R. M., Hassani, M., Bracciale, L., Bianchi, G., Juan-Verdejo, A., Alexakis, S., Gaudino, F., Cascone, D. & Barracano, P., 1 Jan 2020, Digital Transformation for a Sustainable Society in the 21st Century - I3E 2019 IFIP WG 6.11 International Workshops, Revised Selected Papers. Pappas, I. O., Pappas, I. O., Mikalef, P., Jaccheri, L., Krogstie, J., Dwivedi, Y. K. & Mäntymäki, M. (eds.). Springer, p. 72-78 7 p. (IFIP Advances in Information and Communication Technology; vol. 573 AICT).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • 1 Citation (Scopus)

    On Enabling GDPR Compliance in Business Processes Through Data-Driven Solutions

    Zaman, R. & Hassani, M., 21 Jun 2020, In : SN Computer Science. 1, 4, p. 1-15 15 p., 210.

    Research output: Contribution to journalArticleAcademicpeer-review

    Open Access
    File
  • 1 Downloads (Pure)

    Predicting Business Process Bottlenecks In Online Events Streams Under Concept Drifts

    Spenrath, Y. & Hassani, M., 1 Jun 2020, Proceedings of European Council for Modelling and Simulation (ECMS) 2020 . Steglich, M., Muller, C., Neumann, G. & Walther, M. (eds.). European Council for Modeling and Simulation, p. 190-196 7 p. (Proceedings European Council for Modelling and Simulation; vol. 34, no. 1).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Open Access
    File
  • 7 Downloads (Pure)

    Why did my Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data

    Spenrath, Y., Hassani, M., van Dongen, B. F. & Tariq, H., 2020, (Accepted/In press) Proceedings of ECML PKDD 2020 Lecture Notes in Computer Science Springer .

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models

    Cuzzocrea, A., Mumolo, E. & Hassani, M., 16 Jan 2019, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers, p. 2953-2959 7 p. 8616498

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • 1 Downloads (Pure)

    Courses

    DBL Data Challenge

    1/09/15 → …

    Course

    Real-time Process Mining

    1/09/15 → …

    Course

    Student theses

    Analyzing customer journey with process mining: from discovery to recommendations

    Author: Terragni, A., 24 Sep 2018

    Supervisor: Hassani, M. (Supervisor 1), Vitali, M. (External person) (External coach) & Carrá, A. (External person) (External coach)

    Student thesis: Master

    File

    Application of data-driven analytics on sport data from a professional bicycle racing team

    Author: Karetnikov, A., 30 Sep 2019

    Supervisor: Hassani, M. (Supervisor 1) & Nuijten, W. (Supervisor 2)

    Student thesis: Master

    Comparative analysis of standard and process mining-based IT-auditing approaches

    Author: Maliepaard, B., 25 Sep 2017

    Supervisor: Fahland, D. (Supervisor 1), Hassani, M. (Supervisor 2), Dijkman, R. (Supervisor 2) & van der Waerdt, T. (External person) (External coach)

    Student thesis: Master

    File

    Comparing the performance of inclusive OR translations in alignment computation

    Author: van Altena, M., 30 Sep 2019

    Supervisor: van Dongen, B. (Supervisor 1), Hassani, M. (Supervisor 2) & Eshuis, R. (Supervisor 2)

    Student thesis: Master

    File

    Effective steering of customer journeys via context-aware recommendations: introducing OARA, the Order Aware Recommendation Approach

    Author: Goossens, J., 29 Oct 2018

    Supervisor: Hassani, M. (Supervisor 1), Neergaard, M. (External person) (External coach) & Eshuis, R. (Supervisor 2)

    Student thesis: Master

    File