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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

1,076 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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76 — 100 de 279 Avaliações para o Mineração de processos: ciência de dados na prática

por Jani L

19 de out de 2016

Good balance between the more detailed technical stuff and general overview and background. Good quizzes, challenging and relevant to weekly content.

por oscar p

17 de mai de 2022

Very complete course to enter the process mining world. I enjoyed myself a lot while I was doing the course and have put some topics into practice

por Anoop M

27 de nov de 2019

So much research has been done in BPM domain. This course gives a solid foundation in BPM to anyone who wishes to pursue a career in this domain.

por Marcin M

1 de set de 2019

Very interesting course and well done. Descriptions, presentation, and slides are clear. I would for sure search for more courses in this field.

por Jason M C

1 de jun de 2016

An exceptional class that covers a very complex topic in a digestible and usable way. It's a good balance between concept and application.

por Stephen v G

18 de ago de 2019

Complex material, but presented in an understandable way. Assignment was practical. Good integration with open source software packages.

por Tapio H

12 de dez de 2017

Enough but not too much challenge. Surprisingly not so difficult mathematically either. Difficulty between weeks could be more balanced.

por Gelsomina C

2 de mai de 2017

This course is very interesting! A lot of things that I have learnt can be applied to all day life.

The teacher is very nice and clear!

por Bart V d W

26 de jan de 2018

Very clear and thorough explanation of the important concepts of process mining, with enough room for exercises and hands-on practice

por Maros K

15 de fev de 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.

por Caio C d V

12 de nov de 2017

Very useful for those that are seeking knowledge about how to improve processes. I'll use it in my doctorate and also in my work!!!

por Lu Z

10 de set de 2019

Very high-quality course. It is an intermediate level course, so expect some difficulty learning this. But it totally worth it.

por Mariano A M

24 de jul de 2017

Very well thought and laid out course. Examples throughout the lectures clearly illustrate what the Professor wants to convey.

por Juergen S

28 de dez de 2021

v​ery interesting introduction to an, in my understanding, extremely valuable analysis method of e.g. development processes

por Gad S A

16 de abr de 2019

Excellent course, it provided insights into large sets of Data and their structuring, which had not been explored before.

por Abdulrahman A T

25 de jun de 2020

Thanks for the course. This course gives you a good introduction of both business and technical sides of process mining.

por Kerim A

18 de abr de 2019

very informative, amazing content, and definitely worth it. Thanks for offering such an awesome learning opportunity...

por Jean-Paul d V

3 de mai de 2021

Packed with valuable information and reviews of tools that are easy to access and experiment with. Highly recommended!

por towerb

20 de dez de 2020

Prof. van der Aalst is a great lecturer and it is obvious that his team spent a lot of effort to create this course.

por Sergei M

15 de set de 2017

All my expectations were achieved. I like approach of these course, theory was not boring. A lot of practice.


por Marcela G

16 de nov de 2016

Excellent course about Process Mining, it's explained all meant to understand process discovery with Data analysis.

por Frank G

15 de jan de 2017

这门课程十分理论知识丰富,又贴近实际应用,很棒。This course is really fantastic, it both has wonderful academical and practical knowledge.

por Tina H

23 de jan de 2020

It's a great and well-structured course that I can gain fundamental knowledge of process mining quickly. Thanks!

por Gerard H

30 de mai de 2020

Very informative and thorough course about process mining. I will definitely make use of those skills learned.

por José L P

13 de jan de 2022

Very helpful and intuitive course, it´s a must for people in organizations driving Data Mining technology.