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学生对 Eindhoven University of Technology 提供的 Process Mining: Data science in Action 的评价和反馈

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课程概述

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....

热门审阅

GL

May 19, 2021

Great introductory course. The book on which the course is based is a great asset. Very nice to be able to see process mining in action with the tools you can download.

PE

May 18, 2022

Excellent course. Great lectures providing a thorough overview of the topic area. The quizzes do a good job in ensuring that the message has been received.

筛选依据：

276 - Process Mining: Data science in Action 的 300 个评论（共 318 个）

创建者 Olivier S

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Feb 22, 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.

创建者 Francesco C

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May 2, 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.

创建者 Somayeh M

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Dec 30, 2018

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

创建者 G.R. v d A

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Aug 11, 2020

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

创建者 Martin B

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Dec 10, 2018

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

创建者 Pieter V d d

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Oct 24, 2017

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

创建者 Yasuaki M

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Sep 5, 2021

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

创建者 sharath

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Feb 5, 2019

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

创建者 Pedro S

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Feb 3, 2022

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

创建者 Amarildo J F d L

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Jun 15, 2020

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

创建者 Rine l C

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Apr 10, 2021

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

创建者 Alberto C B

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Oct 15, 2017

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

创建者 Felix G

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Jul 25, 2021

some tasks/assignments were a bit cubersome to navigate but overall a great course

创建者 Jesús R S

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Jun 15, 2018

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

创建者 Tania K

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Sep 18, 2017

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

创建者 A M

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Dec 11, 2020

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

创建者 Chow K M

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Jun 23, 2021

More examples to explain calculations would be helpful.

创建者 Schuffenecker

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Dec 3, 2019

a bit academic in the beginning, but really interesting.

创建者 Dardo G O

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Jun 5, 2017

Very interesting, practical and full o information.

创建者 Viktoriia R

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Mar 5, 2019

I think practical tasks in ProM should be included

创建者 Robin G

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Jun 22, 2017

Very clear presentation and a lot of examples

创建者 DHAMMA A

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Jan 22, 2020

NO COMPLICATIONS IN THIS COURSE

创建者 HB S

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Jun 26, 2021

Useful information for my work

创建者 Judy E G S

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Sep 1, 2024

muy completo pero muy teorico

创建者 Lerata M

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Jun 1, 2021

Challenging and fun