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623 Bewertungen
157 Bewertungen

Über den Kurs

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....

Top-Bewertungen

RK

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.

EC

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.

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51 - 75 von 156 Bewertungen für Process Mining: Data science in Action

von Nikolai B

May 27, 2017

This well-focused course provides both theoretical knowledge and practical skills, which could be implemented in real life of most managers (in anersons.

von 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!

von Gilberto A

Oct 31, 2017

Great course, good explanation and excelente selection of topics. Totally recommended!

von Szedelényi J

Jun 02, 2017

Guides through the fundamentals of process mining and provide hands-on skills to apply right away.

von Муратшина А Р

Nov 21, 2016

Very informative, though the accent of the lector is disturbing (my apologies)

von Frank G

Jan 15, 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.

von Gelsomina C

May 02, 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!

von Tobias G

Jul 23, 2017

Superb

von Jingyao L

Aug 24, 2017

这个课程并不难懂。。大师就是大师

von Alejandro

Nov 12, 2017

Great!

von Cafer D

Oct 31, 2017

I like that

von Tapio H

Dec 13, 2017

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

von Paulo A

Jan 08, 2017

Really excellent!

von Jani L

Oct 19, 2016

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

von Tấn T M

May 24, 2017

Excellent course

von Cristiano F

Apr 29, 2017

I learnt a lot from this course. Excellent!

von Bart V d W

Jan 26, 2018

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

von Djana R

Jun 24, 2018

Interesting course. I like it.Recommended.

von Balázs H

Mar 08, 2018

It was very useful and clear to understand course, I would love to have a course with deeper insight on the topic, and one which is just considering the practical use-cases separately, both based on this knowledge.

von 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".

von 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.

von Sergei M

Sep 15, 2017

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

Thanks!

von 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.

von Tom K

Jan 08, 2017

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

von Bronno v d S

May 03, 2017

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