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 7.097A
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

  • JBG030 DBL Data Challenge - The objective of the Data Challenge courses is to teach students how to perform large-scale data-driven analyses themselves, combining the technical skills acquired earlier in the Data Science program with insights gained in methodological courses. Links JBG030 / 2019Q4 @ Osiris Staff
  • 2IAB0 Data analytics for engineers - Learning goals Students gain insight in basic techniques for processing large amounts of data in an efficient, reliable, and consistent way. Students develop skills in understanding, interpreting, and documenting data and information in the context of realistic scenarios. Students get understanding of the data life cycle and develop skills for structuring their solutions of practical Read More ...
  • JM0210 Real-Time Process Mining (JADS) - The Real-Time Process Mining course is an advanced master-level process mining course where the following main contents will be covered: Dimensionality reduction and efficient preprocessing of log files Stream data mining Advanced topics in process mining, like: stream process discovery, online conformance checking and concept drift detection When the focus shifts to advanced topics in 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

  • Differential-private Process Mining (Multiple Assignments) - Within the BPR4GDPR EU project, we are researching (among others) methods that enable a privacy-aware utilization of sensitive individual information. Several anonymization techniques are not enough to completely keep the process discovery completely privacy aware (e.g. the existence of rare diseases can still be revealed from an anonymized log file). Adding exactly “the correct amount” of 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

  • 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

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