|After taking this course students should:|
have a detailed understanding of the entire process mining spectrum and be able to relate process mining techniques to other analysis techniques for business processes,
master various representational biases for process mining (subclasses of Petri nets, structured process models, C-nets, etc.),
understand and apply advanced process discovery techniques based on regions and genetic algorithms,
be able to discuss all four conformance dimensions (replay fitness, precision, generalization, and simplicity), provide metrics for these dimensions, and apply conformance checking using models and logs,
be able to reason about the strengths and weaknesses of existing process mining algorithms and critically evaluate new ones.
|Process mining provides a new means to improve processes in a variety of application domains. There are two main drivers for this new technology. On the one hand, more and more events are being recorded thus providing detailed information about the history of processes. On the other hand, in most organizations there is a need to improve process performance (e.g., reduce costs and flow time) and compliance (e.g., avoid deviations or risks). This advanced course on process mining teaches students the theoretical foundations of process mining and exposes students to real-life data sets to understand challenges related to process discovery, conformance checking, and model extension.|
The course will cover various advanced process discovery techniques, i.e., techniques based on region theory and genetic algorithms. One needs to be able to understand such techniques, apply them, and know their strengths and weaknesses.
The course will also cover conformance checking techniques covering all four conformance dimensions: replay fitness, precision, generalization, and simplicity. A key element is the notion of alignments linking observed to modeled behavior.
Process mining techniques will not be limited to control-flow and will also include other perspectives such as time (bottleneck analysis), resources (social network analysis), and data (decision mining).
Besides learning theoretical concepts, students will be exposed to event data from a variety of domains, including hospitals, insurance companies, governments, high-tech systems, etc. The assignment will either focus on the analysis of such data sets or on focusing on a particular process mining problem.
Note that the bachelor course Business Process Intelligence (2IIE0) introduces process mining at an introductory level. This course is not required as prior knowledge, i.e., 2IMI20 does not depend on 2IIE0. However, students can benefit from 2IIE0 to already have an initial understanding of process mining and being familiar with some of the simpler process mining algorithms.
Entrance requirementsEntrance requirements tests -Assumed previous knowledge
Previous knowledge can be gained byResources for self study
|2ID50 - Datamodelling and databases|
2IIC0 - Business information systems
|Bachelor College or Graduate School||Required materials-Recommended materials|
|Papers, slides, event logs, and exercises are provided via OASE and www.processmining.org.|
|The textbook W. van der Aalst. Process Mining: Data Science in Action. Springer-Verlag, Berlin, 2016 (http://springer.com/978-3-662-49850-7) serves as background information.|
|College / course|
|Number of opportunities||3|
|Opportunities||Block 2, Block 3, Block I|
|Test duration in minutes||180|
RemarkThe mark will be based on an assignment (4 points) and a written exam (6 points, 3 hours). The assignment expires after