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:

  1. Dimensionality reduction and efficient preprocessing of log files
  2. Stream data mining
  3. Advanced topics in process mining, like: stream process discovery, online conformance checking and concept drift detection

When the focus shifts to advanced topics in real-time process mining, we try to bridge the gap between traditional process mining (e.g., process discovery, model optimisation, and conformance checking) and advanced data-oriented techniques (e.g., stream data mining techniques like online classification, stream clustering, and concept drift detection). Advanced process mining techniques can be applied in a variety of domains ranging. Some examples:

  • Analyzing the “customer journey” of customers that have purchased a product and are using related services. How to seduce customers to purchase more services and additional products?
  • Discovering deviations in the company real behaviour from the ideal, GDPR Compliant business process model,
  • Discovering the root causes for delays in treatment processes in a hospital. Which groups of patients are not treated according to the guidelines?
  • Diagnosing the behavior of an X-ray machine that malfunctions and suggesting preventative maintenance. What component should be replaced?
  • Checking the conformance of processes in local governments to find potential cases of fraud. Why was the formal approval step bypassed frequently?
  • Analyzing the study behavior of students following a Massive Open Online Course (MOOC). What are the differences in study behavior between students that pass and students that fail the course?
  • Analyzing a baggage handling system in an airport to understand where luggage gets delayed or misplaced. When and why is the baggage handling system not meeting the service level agreements?
  • Discovering the actual processes supported by a service desk of a large bank. Why does it take such a long time before a person is found that can assist in solving the problem?

The course consists of two tracks:

  1. Track 1 (60% of the final grade): process mining techniques, dimensionality reduction & stream data mining algorithms
  2. Track 2 (40% of the final grade): Practical experience with process mining with a particular focus on analysis  workflows, scientific process mining experiments, and real-world process mining. This track exposes students to real-life benchmark data sets to understand challenges related to process discovery, conformance checking, and model extension.

Recommended literature :

  • Marwan Hassani” Concept Drift Detection Of Event Streams Using An Adaptive Window. ECMS 2019: 230-239
  • Marwan Hassani, Sergio Siccha, Florian Richter, Thomas Seidl: Efficient Process Discovery From Event Streams Using Sequential Pattern Mining. SSCI 2015: 1366-1373
  • Marwan Hassani, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
    On the application of sequential pattern mining primitives to process discovery: Overview, outlook and opportunity identification. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9(6) (2019)
  • Alessandro Terragni, Marwan Hassani: Optimizing customer journey using process mining and sequence-aware recommendation. SAC 2019: 57-65
  • Sebastiaan J. van Zelst, Alfredo Bolt, Marwan Hassani, Boudewijn F. van Dongen, Wil M. P. van der Aalst:
    Online conformance checking: relating event streams to process models using prefix-alignments. Int. J. Data Sci. Anal. 8(3): 269-284 (2019)
  • Selected parts of the textbook Process Mining: Discovery, Conformance and Enhancement of Business Processes by W. van der Aalst. Springer-Verlag, Berlin, 2011 (http://springer.com/978-3-642-19344-6).
  • W.M.P. van der Aalst, A. Adriansyah, and B. van Dongen. Replaying History on Process Models for Conformance Checking and Performance Analysis. WIREs Data Mining and Knowledge Discovery, 2(2):182-192, 2012.Slides, event logs, exercises, and additional papers are provided via OASE and www.processmining.org.
  • Charu C. Aggarwal (Ed.) Data Streams Models and Algorithms. Springer-Verlag 2007 ISBN 978-0-387-47534-9
  • W.M.P. van der Aalst. Process Mining Data Science in Action. Springer-Verlag 2016 Online ISBN 978-3-662-49851-

Links

Staff

  • 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 Read More ...
  • Rashid Zaman - Position: PhD Student Room: MF 7.108 Tel (internal): 6322 Links: Courses Presentations Projects Publications External links: TU/e page Recent courses Recent presentations Recent projects Recent publications  
  • Yorick Spenrath - Position: PhD Student Room: MF 7.122 Tel (internal): 3720 Links: Courses Presentations Projects Publications External links: TU/e page Recent courses Recent presentations Recent projects Recent publications

 

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