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学生对 埃因霍温科技大学 提供的 过程挖掘:数据科学实战 的评价和反馈

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156 个审阅


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....



Jul 02, 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.


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.


126 - 过程挖掘:数据科学实战 的 150 个评论(共 154 个)

创建者 Viktoriia

Mar 05, 2019

I think practical tasks in ProM should be included

创建者 Martin B

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 An N

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.

创建者 Maximilian P

Apr 11, 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

创建者 Rob v d L

Oct 15, 2017

Excellent course!

创建者 Francesco C

May 02, 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 Pieter v d D

Oct 24, 2017

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

创建者 Elia C

Mar 13, 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.

创建者 Robin G

Jun 23, 2017

Very clear presentation and a lot of examples

创建者 Jesús R S

Jun 15, 2018

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

创建者 Gabriel E

Mar 04, 2017

Great course!

创建者 Jelmer J G

Feb 23, 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

创建者 Alberto C B

Oct 15, 2017

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

创建者 Dardo G

Jun 05, 2017

Very interesting, practical and full o information.

创建者 Tania K

Sep 18, 2017

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

创建者 Sameer K

Aug 08, 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.


Apr 15, 2019

Good Course

创建者 Schuffenecker

Dec 03, 2019

a bit academic in the beginning, but really interesting.

创建者 Niko M

Feb 04, 2019

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

创建者 Martin S

Dec 05, 2018

Good introduction to theory of process mining, but most of the techniques are problematic and therefore not practical, and the test questions are tedious as they focus on testing whether you can remember the theory rather than how to apply the theory to real-world problems.

创建者 Henning B

Aug 13, 2017

Interesting material, but the course seems mostly designed to cross-sell the book and promote the (open source) software of the authors, rather then promote understanding of the underlying algorithms.

Positive: The videos go through examples in great detail.

创建者 Mahsa R

May 11, 2018

It is too conceptual. I watched all these long videos and I still don't know how to do a real process-mining project for a client.

创建者 Felipe M P L

Jul 23, 2017

Too much time going over details of the models and not enough on practical use

创建者 Sofie H

Sep 07, 2018

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.

创建者 Alexander B

Oct 21, 2018

Some topics are a bit glazed over and others with concepts that are acknowledged to have major shortcomings (e.g. the alpha algorithm) have a heavy focus in the course and exam despite these shortcomings. Frequent notational switches ("we can automatically change this to ___ ") can make some lectures harder to follow as well, if you're not perfectly versed in some of the leveraged notations in this course. OK overall.