Chevron Left
Back to Process Mining: Data science in Action

Process Mining: Data science in Action, Eindhoven University of Technology

473 ratings
122 reviews

About this Course

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. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action". The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains. 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....

Top reviews


Jul 31, 2017

Great course. Professor Wil van der Aalst delivers great lectures, very clear and deep in general with good examples. I really enjoyed the course from the beginning to the end.


Jan 29, 2018

This course was wonderful. I have attempted it several times, but did not find enough time to finish it until lately. Dr. van der Aalst is magnificent in his presentation.

Filter by:

120 Reviews

By Maros Kolibas

Feb 15, 2019

Great course, it covers basics of process mining, from petri net, over pm algoritms to steps how to do process mining on real data.

By Klim

Feb 12, 2019

The course material was very well explained during the lectures. The course gave a very good overview of the PM field and its practical applications.

By An Nguyen

Feb 06, 2019

The course is a very nice introduction. I would have liked to give more additional hints to more advanced methods for an audience interested in perusing a PhD in this field. E.g. some optional implementation tasks/project would have been nice.

By sharath

Feb 05, 2019

Gives a solid foundation for the process mining concepts!! Explained in depth by a wonderful professor.

By Niko Marola

Feb 04, 2019

Very good course. More real life cases and process mining examples would be beneficial.

By Ahmed Eid

Feb 03, 2019

it's amazing <3

By Alexey Gy

Jan 29, 2019

Great overview of the Process Mining field. Easy to follow and very intuitive course material. Great usage of exercises and examples. Helpful practical introduction to Process Mining tools.

By Davide Dozza

Jan 18, 2019

Perfectly fit my expectations.

By Christos Hadjiinikolis

Jan 06, 2019

Very informative course.

By Somayeh Mahmoodi

Dec 31, 2018

Thanks to Prof. Wil Van Der Aalst and his team for providing me with the opportunity of learning process mining. That was terrific!