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Process Mining Data science in Action

Course Summary

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

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    Course Syllabus

    This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments. The course covers the three main types of process mining. 

    1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log. 
    2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa.
    3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases.

    Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development. The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field.After taking this course you should:- have a good understanding of Business Process Intelligence techniques (in particular process mining),- understand the role of Big Data in today’s society,- be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification,- be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools),- be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools),- be able to extend a process model with information extracted from the event log (e.g., show bottlenecks),- have a good understanding of the data needed to start a process mining project,- be able to characterize the questions that can be answered based on such event data,- explain how process mining can also be used for operational support (prediction and recommendation), and- be able to conduct process mining projects in a structured manner.

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    Recommended Background

    A basic understanding of logic, sets, and statistics (at the undergraduate level) is assumed. Basic computer skills are required to use the software provided with the course (but no programming experience is needed). Participants are also expected to have an interest in process modeling and data mining but no specific prior knowledge is assumed as these concepts are introduced in the course. 

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    Course Format

    • The course consists of 6 weeks. Every week consists of a series of short lecture videos (called modules) of about 8-15 minutes each. 
    • Reading assignments are provided for every week.
    • Weekly quizzes (multiple choice, online) to test your understanding of the lecture videos of that week.
    • Final exam (multiple choice, online).
    • A peer assignment in which you apply the tools and techniques on real data and make a short report (not mandatory for normal certificate).
    • A tool quiz to help you get acquainted with the tools used in this course (not mandatory for normal certificate).
    • Forum discussions.

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    Suggested Reading

    No required texts. Although the lectures are designed to be self-contained, we recommend (but do not require) that students use the book "Process Mining: Discovery, Conformance and Enhancement of Business Processes by W.M.P. van der Aalst, Springer Verlag, 2011 (ISBN 978-3-642-19344-6)", which is closely aligned with this course. One can read the process mining manifesto or visit the web site to see more background material.

Course Fee:

Course Type:


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1 - 4 hours / week

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