Dr. Marwan Hassani

Hi! I am an assistant professor at the Analytics for Information Systems Group in the Department of Computer Science, Eindhoven University of Technology (TU/e). Previous to that, I worked as a postdoc at the Data Management and Data Exploration Group at RWTH Aachen University, Germany.
My research areas are: data mining, data science, process mining and big data analytics.
Current interests are: stream data mining, real-time process mining, sequential pattern mining, subspace clustering and evolving graph mining.

News:

  • (July 2019) I was awarded the University Teaching Qualification (UTQ) based on my dossier. This award proves my capabilities in: (a) designing and implementing teaching, (b) testing and assessing, (c) organizing and coordinating teaching, (d) evaluating teaching and (e) professionalization.
  • BPM Demo 2019 (July 2019) Our demo: "Online Comparison of Streaming Process Discovery Algorithms" has been accepted in BPM 2019. The tool enables a simultaneous comparision of the outputs of two stream process discovery algorithms with a MOA-like feeling in ProM for a better understanding of the effects of parameter setting. Congratulations to my master student Kavya Baskar.
  • WIREs Journal 2019 (May 2019) Our article: "On the application of sequential pattern mining primitives to process discovery: Overview, outlook and opportunity identification" with Sebastiaan van Zelst and Wil van der Aalst is published as a focus article at WIREs Data Mining and Knowledge Discovery.

  • BD Clustering BookMy Book Chapter(January 2019) My book chapter: "Overview of Efficient Clustering Methods for High-dimensional Big Data Streams" has finally appeared in the book titled: "Clustering Methods for Big Data Analytics" Edited by Olfa Nasraoui and Chiheb-Eddine Ben N'Cir.


  • DBL DC Excellent Evaluation(January 2019) Honored to receive an excellent student evaluation for the DBL Data Challenge course I taught in the data science bachelor. Highly motivated students made the teaching experience of this course even more enjoyable.



  • BPR4GDPR Brochure(December 2018) We have published a new brochre of our EU H2020 project: BPR4GDPR. Please find it (here).



  • (November 2018) Our paper on sequence-aware recommendation in customer journey using process mining was accepted at the 34th ACM/SIGAPP Symposium on Applied Computing (SAC'19). Congratulations to my student Alessandro Terragni for an excellent master project!
  • BPIC2018 Winning Student Report(August 2018) Proud to know that my 2nd year Big Data Honors students: Jarno Brils, Nina van den Elsen, Jan de Priester and Tom Slooff were selected the winners of the BPI 2018 Challenge in the student category for their report: “Business Process Intelligence Challenge 2018: Analysis and Prediction of Undesired Outcomes”. These 3rd year bachelors students competed internationally with other students (including PhDs) using the results I coached them to produce on top of their majors. Congratulations!
  • (June 2018) My book chapter: "Overview of Efficient Clustering Methods for High-dimensional Big Data Streams" will appear in the book titled: "Clustering Methods for Big Data Analytics" in October 18, 2018, ISBN 978-3-319-97863-5
  • (May 2018) A PhD vacancy on privacy-aware stream mining under my supervision is open at Eindhoven University of Technology. Apply now under this link! The selection process with start in June 2018 and will continue until the position gets filled.
  • (April 2018) I am co-chairing the KDD BigMine 18: the 7th International Workshop on Big Data on Streams and Heterogeneous Source Mining - A full-day KDD 2018 Workshop in London. Submit your paper now under: https://bigmine.github.io/bigmine18/submission.html/.
  • (February 2018) The Data Tales research consortium The Data Taleshas been granted an NWO Creative industry - Knowledge Innovation Mapping (KIEM) funding. Congratulations to all partners!
  • (January 2018) My EU H2020 IA project: BPR4GDPR has been granted! Announcement on TU/e M&CS Intranet Congratulations to all partners! Two full PhD positions at TU/e beginning from May 2018 are funded. There will be soon a call for the PhD position on stream process mining under my supervision! The project full name is: "Business Process Re-engineering and functional toolkit for GDPR compliance". In the call, only 13 proposals out of 82 were granted and BPR4GDPR scored: 14/15 (Excellent). Find here a link to the detailed announcement on the department intranet.
  • (January 2018) I am chairing the Data and Knowledge Management track of the FiCloud 2018, The IEEE 6th International Conference on Future Internet of Things and Cloud in Barcelona. Submit your paper now under:http://www.ficloud.org/2018/.
  • (December 2017) A new journal article: Marwan Hassani, Daniel Töws, Alfredo Cuzzocrea and Thomas Seidl BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams. In the International Journal of Data Science and Analytics https://doi.org/10.1007/s41060-017-0084-8
  • Publications:

    See my publications here:

    Teaching Activities:

    Courses 2018-2019

    JM0210 - Real-Time Process Mining
    JBG030 - DBL Data Challenge
    HA700 - Big Data Honors Track
    2IAB0 - Data Analytics for Engineers

    Courses 2017-2018

    2IAB0 - Data Analytics for Engineers
    JM0210 - Data-Driven Business Process Management
    HA020 - Big Data Honors Track
    JBG030 - DBL Data Challenge

    Courses 2016-2017

    HA020 - Big Data Honors Track
    2IIC0 - Business Information Systems

    Reviewing Activities:

    Program committee chair in:

  • KDD BigMine 18 & 19 , the 7th & the 8th International Workshop on Big Data on Streams and Heterogeneous Source Mining - KDD 2018 & KDD 2019 Workshops.
  • FiCloud 2018 & 2019 , The IEEE International Conference on Future Internet of Things and Cloud, Data and Knowledge Management track.
  • Proceedings Editor of:

  • LWDA 2014, The German Machine Learning Workshops on "Lernen, Wissen, Daten, Analysen"
  • Program committee member in:

  • SDM (SIAM International Conference on Data Mining),
  • INS (Information Sciences Journal),
  • KAIS (Knowledge and Information Systems Journal),
  • DMKD/DAMI (Data Mining and Knowledge Discovery Journal),
  • PLETTERS (Pattern Recognition Letters),
  • JMLR (Journal of Machine Learning Research),
  • Elsevier's Pervasive and Mobile Computing Journal (Special issue on Information Management in Mobile Applications),
  • VJCS (Vietnam Journal of Computer Science),
  • ACI (Applied Computing and Informatics Journal),
  • ECIS The European Conference on Information Systems,
  • BigMine The International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (in conjunction with KDD),
  • BigMM The International Conference on Multimedia Big Data,
  • CBDCom The IEEE International Conference on Cloud and Big Data Computing,
  • SML Workshop on Scalable Machine Learning Co-located with the INNS Big Data conference,
  • AINA The International Conference on Advanced Information Networking and Applications,
  • EIDWT The International Conference on Emerging Internetworking, Data & Web Technologies, special session on Machine Learning on Large Data Sets & Massive processing,
  • MOBILWARE The International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications,
  • KDML Workshop on Knowledge Discovery, Data Mining and Machine Learning @ LWA,
  • HPCC The IEEE International Conference on High Performance Computing and Communications - Track: Database Applications and Data Mining,
  • IMMoA The International Workshop on Information Management for Mobile Applications (in conjunction with VLDB)
  • MCCTA The International Workshop on Mobile Cloud Computing Technologies and Applications (in conjunction with NBiS)
  • SDAD The International Workshop on Sentiment Discovery from Affective Data (in conjunction with ECML PKDD)
  • IMMoA The International Workshop on Information Management for Mobile Applications (in conjunction with VLDB)
  • SensorKDD The International Workshop on Knowledge Discovery from Sensor Data (in conjunction with KDD)
  • Contact information

    Mailing address: Eindhoven University of Technology
    Department of Mathematics & Computer Science
    P.O. Box 513
    5600 MB Eindhoven, The Netherlands
    Office address: MetaForum 7.097a
    De Groene Loper 5
    5612 AZ Eindhoven, The Netherlands
    telephone: +31 (0)40 247 3887