After taking this course students should:
- be able to relate process mining techniques to other analysis techniques (data mining, model checking, simulation, etc.),
- understand the positioning of process mining in the context of data science,
- be able to apply a range of process mining techniques and use tools such as RapidMiner, ProM, and Disco,
- be able to design analysis workflows and execute them using process discovery and conformance checking techniques on concrete practical datasets (e.g., using RapidMiner and ProM),
- be able to reason about the strengths and weaknesses of existing process mining algorithms and critically evaluate new ones,
- be able to use process mining for comparing subgroups of cases and process variants (comparative process mining),
- compresence the concept of real-time (stream) data mining, and be able to execute available stream clustering and stream classification algorithms
- describe the dimensionality reduction concept and differentiate between dimensionality reduction techniques
- be able to identify the business value of real-time process mining,
- be able to conduct real-world process mining projects (e.g. customer journey optimization) using real data and imprecise questions from stakeholders.
Highly recommended literature:
- 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.
- Aggarwal, Charu C. (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 (https://link.springer.com/book/10.1007%2F978-3-662-49851-4)
|The course starts with an overview of the BPM domain using a set of twenty BPM use cases. These cover four key BPM activities: model (creating a process model to be used for analysis or enactment), enact (using a process model to control and support concrete cases), analyze (analyzing a process using a process model and/or event logs), and manage (all other activities, e.g., adjusting the process, reallocating resources, or managing large collections of related process models).|
Then the focus shifts to process mining. Process mining bridges the gap between model-based process analyses (e.g., simulation, model checking, and classical BPM techniques) and data-oriented techniques (e.g., data mining techniques like classification, clustering, and regression). Process mining techniques can be applied in a variety of domains ranging. Some examples:
- 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?
- 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?
- 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?
Particular emphasis will be on the performance/management side of process mining and the creation of repeatable analysis workflows using RapidProM.
The course consists of two tracks.
Track 1: Business Process Management and Process Mining Techniques (based on selected papers). Track 1 is assessed by means of a final written test (40%). The track focuses on topics such as the BPM Use Cases, process modeling, simulation, process discovery, conformance checking, performance analysis, process cubes, and prediction
Track 2: Practical hands-on 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 data sets to understand challenges related to process discovery, conformance checking, and model extension. Track 2 is examined by means of an assignment that consists of three parts (60%).
Type of instructionsLectures and instructions
Type of examsTrack 1: written test (40%)
Track 2: three-part assignment (60%)
|Written test opportunities|
|Written test opportunities (HIST)|
|Schriftelijk (60%) / Written (60%)||EXAM_01||SM 1||1||09-12-2020|
|Schriftelijk (60%) / Written (60%)||EXAM_01||SM 1||2||13-01-2021||Required materials-Recommended materials-Tests|
|2 Presentations (40%)|