RWTH Process Mining Lecture 1: Introduction to Process Mining

RWTH Process Mining Lecture 1: Introduction to Process Mining
Lecturer: Prof.dr.ir. Wil van der Aalst (www.vdaalst.com, @wvdaalst)
In this lecture, Wil van der Aalst presents an overview of the topic of process mining in a top-down and bottom-up manner
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More about this Process Mining Course @ RWTH Aachen University (BPI 2021):
This course consists of around 20 lectures covering the different fields of process mining, including five process discovery techniques, three conformance checking techniques, data preparation, decision mining, predictive analytics, machine learning, big-data analytics, and process mining software. The course is at an introductory level, but also comprehensive and providing details on state-of-the-art process mining techniques.
The videos are part of the Business Process Intelligence (BPI) course organized by the PADS (Process and Data Science) group at RWTH (www.pads.rwth-aachen.de) and led by prof.dr.ir. Wil van der Aalst. The lectures by Wil van der Aalst are online due to COVID-19 and recorded in Spring 2021. Niklas Adams, Bianka Bakullari, Ali Norouzifar, Marco Pegoraro, Mahsa Pourbafrani, Mahnaz Qafari, Majid Rafiei, and Miriam Wagner took care of the instructions and assignments.
For more information, visit www.vdaalst.com and www.pads.rwth-aachen.de.
Enjoy the course! We hope it will inspire you to dive deeper into this wonderful world of process mining.
Overview
1 Introduction to Process Mining
2 Decision Trees
3 Association Rules & Clustering
4 Introduction to Process Discovery
5 Petri Nets & Alpha Algorithm
6 Alpha Algorithm Continued
7 Quality of Discovered Models and Representations
8 Heuristic Mining
9 Region-Based Mining
10 Inductive Mining
11 Event Data and Exploration
12 Conformance Checking (1/2)
13 Conformance Checking (2/2)
14 Decision Mining
15 Organizational Mining & Bottleneck Analysis
16 Refined Process Mining Framework and Operational Support
17 Dealing with Big Event Data
Closing Lecture
(Note that the RWTH course has more lectures dedicated to the organization, exam, assignments, etc. Since these are specific, they are not included.)
See • Process Mining Course ... for all lectures in this course. See • BPI Course - An introd... for last year's course (BPI 2020) and • Introduction to Data S... for an Introduction to Data Science (IDS 20/21).
WWW: www.vdaalst.com
Twitter: #processmining @wvdaalst
LinkedIn: linkedin.com/in/wvdaalst

[@pomethz]

This is a great introduction to Process Mining, and straight from the source! Thank you!

[@geralddivinagracia2349]

Thank you Prof. Van der Aalst for sharing your expertise! Looking forward to the rest of the sessions!

[@LukasSoftwareDeveloper]

oh wow Prof.dr.ir. Wil van der Aalst. I working with my masters and most of the time I hit your books. That's huge discovery that I hit your lectures. Thank you!

[@TeYaKo]

Thank you so much for all the information, that you share. You help me to improve my professionality.

[@user-wl6qm6td2e]

Thank you for your excellent lectures, it's really good for me to understand Process Mining!!!

[@wilvdaalst]

Thanks!

[@returntofleet]

Thank you for sharing the course lectures here.

[@vishnunallani]

Very beautifully explained

[@smiles7546]

Great Lecture :) thank you very much

[@parthsharma9194]

Great tutorial! Been researching on process mining for some time.
Would love to work with you someday!

[@wilvdaalst]

Thanks.

[@PlayfulPie]

are the slides available for download anywhere?

[@peacetumetoh6589]

thank you great Prof for this. Information on this out there is a bit scarce. im happy i found this

[@Yaxoi]

He also wrote a great (i.e. the) book on the topic

[@I_agree_with_you]

Thank you..

[@christbryannokamfotso3252]

Please I would like to know a process enhancement tool

[@wilvdaalst]

The more mature higher-end process mining tools privide automation capabilities, e.g., Celonis embeds the Make/Integromat low-code automation tools. This way compliance and performance gaps are translated into actions in the source systems.