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 978-3-662-49851-4

Links

• Osiris (at Tilburg University)

Staff