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Voltar para Mineração de processos: ciência de dados na prática

Comentários e feedback de alunos de Mineração de processos: ciência de dados na prática da instituição Universidade Tecnológica de Eindhoven

1,077 classificações

Sobre o curso

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....

Melhores avaliações


1 de jul de 2019

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.


9 de dez de 2019

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.

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226 — 250 de 279 Avaliações para o Mineração de processos: ciência de dados na prática

por Elia C

13 de mar de 2017

Extremely interesting subject. The exposition is for the most part remarkably clear, though it amounts to a necessarily quick introduction to the vast variety of tecniques and tools. The student is supposed to conduct a lot of practice as self-study and experimenting. Giving 4 stars out of 5 instead of full marks only because I feel this course might work better in an extended format, going more in-depth on real cases and guiding the students through the hurdles and subtleties of such analyses.

por Wasay A

19 de dez de 2020

A very information course of a new and currently developing field of process mining. Every bit of information was elaborate and clear. As an advisory associate, it had practical benefits to my own work as well. However, the only possible thing that lacked was ways to acquire the data or cleaning it to be put into the data mining software. My practical application of the concepts taught have been greatly restricted due to that.

por José F C

25 de mai de 2020

Comprehensive dive into the world of Process Mining, from the most basic concepts into advanced topics, such as applying Process Mining to live data. Unfortunately, while I leave this course with a solid foundation, I wish there were tutorial lectures dedicated to go through practical exercises (namely in ProM and/or Disco). Great course overall.

por Nathan L

6 de set de 2020

Very detailed, thorough, and comprehensive. However, reason why I gave a 4 is because I felt some areas were a bit difficult to understand. Because I have a background in process improvement and data analytics, I was able to navigate and figure out the content, but for someone without this knowledge, it could get overwhelming.

por Sven S

14 de dez de 2019

The content of the course was excellent. The Android app is a little bit poor, especially if one would do the quizzes. The pictures are not very well to watch. For some topics (e.g. Alpha Algorithm) I would have appreciated it, if you could replay an example very clearly and in a deep detail.

por Jasper v H

19 de mar de 2020

Good short course to get introduced into the formalism of process mining. Beginning might be a bit overly formal with not much justification for why this formalism is best. Could use a bit more real-world examples at the end, and how data science and machine learning can help.

por An N

6 de fev de 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.

por Raphael F

14 de jul de 2022

Curso bem completo, com certa profundidade de análises, com cálculos complexos.

Bastante interessante para pessoas que trabalham ou se preparam para trabalhar com Process Mining.

O professor é bastante didático e claro em suas explicações.

por Theo d K

21 de jan de 2020

Good course, interesting subject. Sometimes examples are presented too quickly, without explaining where certain numbers come from. References to chapters in the book refer to another edition of the book (no biggie)

por Martin L

26 de out de 2020

An excellent course to get a broad overview of the innerworkings of process mining. Somewhat more on the technical side so I strongly recommend doing the honors assignments to get to the practical aspects.

por Maximilian P

11 de abr de 2019

The topics covered in the course were very interesting, though the course would have been more valuable if accompanied with python programming of case studies.

Kind regards Max

por Sameer K

8 de ago de 2018

Good introductory course to data mining. It would help if the disco demo version has a higher limit ( >100 lines) as that would allow better experimentation with real data.

por Jelmer J G

23 de fev de 2018

I would like to have 1 more week in which one can go through a process mining process from start to end, step by step. Furthermore, great course, very hands-on and useful

por Olivier S

22 de fev de 2021

Very interesting course, provides a lot of valuable background and insights on process mining, but as always, it will take a lot of practice to be able to create value.

por Francesco C

2 de mai de 2018

Not that difficult, but it gives the right instruments to understand how things are related into a process and how it can be described starting from logs.

por Somayeh M

31 de dez de 2018

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

por GR v d A

11 de ago de 2020

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

por Martin B

10 de dez de 2018

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

por Pieter V d d

24 de out de 2017

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

por Yasuaki M

5 de set de 2021

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

por sharath

5 de fev de 2019

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

por Pedro S

3 de fev de 2022

I think that the course requires a specific module of practices to get a more practical learning.

por Amarildo J F d L

15 de jun de 2020

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

por Rine l C

10 de abr de 2021

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

por Alberto C B

15 de out de 2017

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