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Process Mining: Data science in Action(으)로 돌아가기

아이트호벤 공과 대학의 Process Mining: Data science in Action 학습자 리뷰 및 피드백

598개의 평가
150개의 리뷰

강좌 소개

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....

최상위 리뷰


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.

필터링 기준:

Process Mining: Data science in Action의 147개 리뷰 중 26~50

교육 기관: Najmeh R

Oct 22, 2016

Excellent! Well defined, practical examples and also it shows how it can be used be Prom tool.

교육 기관: aniket n

Oct 16, 2017

Learned a lot of new concepts. :)

교육 기관: Marcela G

Nov 16, 2016

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

교육 기관: Alix C

Mar 29, 2018

Easy to understand and very comprehensive. Examples are challenging but help to understand everything.

교육 기관: Josef M

May 17, 2018

Very good course

교육 기관: Jason M C

Jun 02, 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.

교육 기관: Sergei M

Sep 15, 2017

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


교육 기관: Rony S

Aug 20, 2017

In depth course for process mining. Anyone trying to jump into a career on Business processes, or wants to apply data science to business processes, should take this course. It is more involved than other Data Science course, so give it your all.

교육 기관: Tom K

Jan 08, 2017

Very good overview and provides a good foundation for further exploration in Process Mining.

교육 기관: Michelle T

Apr 25, 2018

This is a very good course for those who are interested in process mining. I continue to review and improve my understanding on each concept, and one day I will be able to reap the fruit of all the process improvements through applying this in work place. Thank you very much for offering this course!

교육 기관: Kirill D

Jan 28, 2018

Great course! Well balanced theoretical information and practical exercises. Algorythms were explained in easy for understanding way. Thank you very much, Wil van der Aalst, Joos Buijs, and the rest of the Process Mining team!

교육 기관: 國人 吳

Aug 20, 2016

The best lecture

교육 기관: John R

Nov 13, 2016

Awesome Course, great lectures, the data that is available to use for ProM and Disco really made the difference. I would recommend this course for anyone interested in process analytics or Lean/ Six Sigma business process optimization.

교육 기관: Mariano A M

Jul 24, 2017

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

교육 기관: Rodrigo C

Apr 01, 2018

This course is very useful. Its content give us a clear notion of process mining and how to apply it to discover the process model.

It helped me identifying real cases bottlenecks in my own process and my analysis are more data-based. This chance in my approach made my work more reliable and "to the point".

교육 기관: Bronno v d S

May 03, 2017

Great lectures, great insights and very helpful in my professional life.

교육 기관: Rafael R

Jul 06, 2017

It's such a good course!

교육 기관: Marcilio J A G F

Jan 22, 2018

Excelente curso introdutório. Desperta a curiosidade para aprofundamento na área.

교육 기관: Ivan A

Feb 23, 2018

Excellent course! I really liked how the complex nature of Process Mining is explained with examples.

Both theoretical and practical sides of Process Mining are explained.

References to more specialized and advanced materials were given so that one can further research for particular needs.

Great work Wil!

I would really enjoy to see a course like "Comparative Process Mining" or "Advanced Practical Process Mining Applied" from you.

Thank you very much!

교육 기관: Nikita

Jun 04, 2018

Thanks a lot. It was very usefull!

교육 기관: Matthew W

Jul 12, 2016

Excellent Material and presentation standard. Very clear and educational

교육 기관: Onur D

Jun 06, 2017

I'm very glad to participate in the course. I decided to use Process Mining in my PhD thesis. Thank you Prof. van der Aalst. I hope, we meet one day.

교육 기관: Willem R

Feb 26, 2018

Very interesting as an introduction to Process Mining. I believe the course laid the right foundation to understand the functioning of process mining software such as Disco and ProM.

교육 기관: Maxim C

May 15, 2018

Great course about general principles of process mining! It gives many insights. Consideration of PM tools is very useful. Thanks!

교육 기관: Alessandro T

May 13, 2018

Very interesting course, explained in a understandable way and rich of high level topics. Essential for anyone who likes statistics and process analysis. Many congratulations for it!