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Process Mining: Data science in Action に戻る

アイントホーフェン工科大学(Eindhoven University of Technology) による Process Mining: Data science in Action の受講者のレビューおよびフィードバック



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....



The course is designed and presented by professor aptly for beginners. I think before reading the Process Mining book it is good to take this course and then read the book later. The quizzes are good.


Good content, very thorough, and I learned a LOT! Took more time than suggested, as I learn by taking notes and reproducing diagrams. But the course structure allowed for frequent pauses to do this.


Process Mining: Data science in Action: 226 - 250 / 263 レビュー

by Somayeh M


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

by GR v d A


Course is very clear, education level is high and also pragmatic. Very good start to understand and execute process mining.

by Martin B


There should be a mandatory data science Project to make the students experience the practical side process mining projects

by Pieter V d d


Very interesting course. Last two weeks put quite some emphasis on advertising tools instead of explaining them thoroughly.

by Yasuaki M


N​eeds some kind of mathematical skills. And If you want to learn practical skills, you should move to week5 and week6.

by sharath


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

by Amarildo J F d L


O fato de não ter tradução para português impactou bastante na compreensão de algumas atividades

by Rine l C


Very interesting course. Theory in the book goes quite deep, but it shows a lot of potential.

by Alberto C B


Best course out there in Process Mining! The professor explains the topic in a very good way.

by Felix G


s​ome tasks/assignments were a bit cubersome to navigate but overall a great course

by Jesús R S


Good approach to an interesting topic and extensive practise exercises with tools.

by Tania K


Great course. A lot of academic knowledge but also covers practical experience

by A M


Good course. Gives a nice overview over the topic Process Mining

by Chow K M


M​ore examples to explain calculations would be helpful.

by Schuffenecker


a bit academic in the beginning, but really interesting.

by Dardo G


Very interesting, practical and full o information.

by Viktoriia


I think practical tasks in ProM should be included

by Robin G


Very clear presentation and a lot of examples




by Eric S


Useful information for my work

by Lerata M


Challenging and fun

by Rob v d L


Excellent course!

by Gabriel E


Great course!



Good Course

by Sofie H


Sometimes too technical. It would have enjoyed it more if there would have been the possibility to choose which aspects of Process Mining I was interested in. In one of the last lessons, I came to the understanding that I want to apply process mining to spaghetti-structured event data, therefore I had to learn a lot about prediction, recommendation and so on which than turned out to be completely useless for me. I have the same feeling for the petri net, workflow syste, BPMI and so on that are presented; this is only useful for some users while this takes a large part of the study time. It may thus be recommended to organize a more 'practical' PM course for users interested in using Disco and a more technical course for users interested in more advanced analysing techniques.