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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

599개의 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 51~75

교육 기관: Geoff A

Jul 20, 2017

Excellent introduction to the topic of process mining. The delivery of the course was very easy to use. The course notes were excellent. Thanks very much to Professor van der Aalst for sharing his knowledge.

교육 기관: Deleted A

Sep 17, 2017

Good start to Process Mining

교육 기관: Pascal F

Mar 09, 2018

Great introduction to Process mining with practical applications incl

교육 기관: Rob B

Oct 10, 2016

Great course and very nice video's lectures!

교육 기관: Pablo I R P

Oct 25, 2017


교육 기관: Sudhanshu S

Jan 03, 2017

The best course on Process Mining from the best place in Process Mining.

교육 기관: Greg L

Jan 05, 2017

very comprehensive. well structured. good pace, I would recommend having the book for reference/research.

교육 기관: Evgeny I

Jun 11, 2017

Rather complicated but great course

교육 기관: Yosef A W

Jan 21, 2018

Easy-to-understand with useful examples, and also process mining is a technique that is applicable to many cases.

교육 기관: Jiaxin C

Oct 04, 2017

Outstanding course structure, even for someone like me that have absolutely no background in process mining, to learn so much in this course:)

교육 기관: Niyi O

Apr 21, 2017

Brilliant course. Would fully recommend

교육 기관: Michael S T

Jan 29, 2018

This course was wonderful. I have attempted it several times, but did not find enough time to finish it until lately. Dr. van der Aalst is magnificent in his presentation.

교육 기관: Mohammad R H N

May 30, 2018

This course was very applicable and helpful for me.

교육 기관: Secundino S

May 28, 2017

Fantastic way to get additional insights through data mining on digitized processes

교육 기관: Caio C d V

Nov 13, 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!!!

교육 기관: Pavel Z

Mar 06, 2017


교육 기관: shiyangqi

Jan 26, 2017

the course looks into process analysis, which is also a very important section of data science!

교육 기관: Bo P

Oct 04, 2016


교육 기관: simofura

Sep 20, 2016

Great great course.

I'm a beginner in this matter so to me there are cases difficult to understand. One thing that could help a lot would be more exemplas of real life for each theorical concept. As done at the beginning with coffee, latte, muffin, ect.

Thanks for the energy dedicated to create this course.

교육 기관: Enrique C

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.

교육 기관: Ahmed T

Jan 22, 2017

Amazing Course :)

교육 기관: Edwin V

May 28, 2018

Best online course Ive ever taken. Great details and lots of specific examples. Perfect theory and practice balance. Really satisfied! Congratulations for you for this example of how to set an online course!

교육 기관: Pierre-Yves N

Jul 16, 2017

Excellent balance between theory, simple examples and real-life situations. Language is clear and the level of complexity appropriately defined. Two thumbs up!

교육 기관: Yoon P

Apr 24, 2016

Wow! Changing my life and career, this course does.

교육 기관: Diego M

Nov 13, 2016

Very interesting!!! A good approach to Process Mining!!!