Back to Process Mining: Data science in Action
Eindhoven University of Technology

Process Mining: Data science in Action

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.

Status: Data Mining
Status: Data Processing
IntermediateCourse22 hours

Featured reviews

OD

5.0Reviewed Jun 5, 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.

PE

5.0Reviewed May 18, 2022

Excellent course. Great lectures providing a thorough overview of the topic area. The quizzes do a good job in ensuring that the message has been received.

GL

5.0Reviewed May 19, 2021

Great introductory course. The book on which the course is based is a great asset. Very nice to be able to see process mining in action with the tools you can download.

SK

4.0Reviewed Aug 7, 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.

MP

4.0Reviewed Apr 10, 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

AD

5.0Reviewed Jan 1, 2022

Very well structured curse. Perfect introduction. Perfect level of detail when you want to know and do not have some but not solid knowledge in the Algorithms and academic base.

VK

5.0Reviewed Sep 22, 2024

This is a detailed course with reasonable level of effort required to complete successfully. It is well laid out and I found the coursework to be helpful.

DH

5.0Reviewed Aug 31, 2020

Es un curso desafiante. Felicito a todo el equipo detrás del mismo, muy interesante todo lo expuesto y las lecciones son excelente. Sé que me será muy útil en mi carrera.

MF

5.0Reviewed Nov 9, 2022

Thank you Professor dr. Jr. Wil Van Der Aalst for the deep foundations and applied skills that we developed through Process Mining: Data and Process Science in Action.

CJ

5.0Reviewed Apr 24, 2020

Very well planned and delivery by Prof was exceptional! I will definitely be interested to learn of more such courses from the university of Eindhoven

SW

5.0Reviewed May 5, 2020

Very in-depth and well-presented material. The quizzes are quite involved, but form a rigorous basis for testing comprehension and ability to apply material.

JL

5.0Reviewed Oct 18, 2016

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

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