Process Mining
News
- January 14th, 2019: On Thursday January 17th there will be no lesson
- October 25th, 2018: Dear student due to an unforeseen engagement I cannot deliver today's lesson.
- October 2nd, 2018: Next lesson will be delivered on October 4th
General Info
Teachers:
- Andrea Polini, Barbara Re
ESSE3 Link
Lessons Scheduling:
- 42 h - lecture and exercise sessions
- Monday: 2:00 PM – 4:00 PM
- Thursday: 2:00 PM – 4:00 PM
Course Objectives
- This course focuses on the the missing link between model-based process analysis and data-oriented analysis techniques
- The course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains
- The course explains various process discovery algorithms,
- The course introduce conformance techniques to compare processes and event data
- The course provides to students the opportunity to experiment with real tools
Learning Outcomes
- At the end of the course, the students will gain familiarity with process mining terminology, methodology and technologies
- Be able to apply basic process discovery techniques to learn a process model from an event log
- Be able to apply basic conformance checking techniques to compare event logs and process models
- Have a good understanding of the data needed to start a process mining project
- Be able to conduct process mining projects in a structured manner
Course Contents
- Introduction to Process Mining and Data Mining. Data. Logs. Play-in, Play Out and Reply. Fundamentals of Process Modelling.
- From Event Logs to Process Models. Getting Data. Process Discovery. Discovery Techniques an Introduction. Tools support an introduction: Apromore, Disco, and ProM.
- Discovery Algorithms. Alfa, Heuristic, Genetic Miner.
- Conformance Checking. Business Alignment and Auditing. Token Replay. Process Drift. Business Process Model and Instances. Business Process Life-Cycle. Classification of Business Process.
Study material
Classes Scheduling
Course Slides
Streaming
- October 8th, 2019
Reference book:
- Process Mining: Data Science in Action by W.M.P. van der Aalst, Springer Verlag, 2016 (ISBN 978-3-662-49850-7). From chapter 1 to 9.
Tools:
- Apromore - http://apromore.unicam.it/
- Disco - https://fluxicon.com/disco/
- ProM - http://www.promtools.org/
Exams
Project Based on 2018 Business Process Intelligence Challenge (BPIC) - https://www.win.tue.nl/bpi/doku.php?id=2018:challenge
Project Based Examination on the topics of the syllabus including project presentation
- Process Mining Challenge: (i) we will provide real data set, (ii) we will provide questions to be answered
- We expect that you can focus on a specific aspect of interest and analyze this aspect in great detail using any technique, method, algorithm, and tool
- We will judge based on: (i) the originality of the results, (ii) the validity of the claims, and (iii) the depth of the analysis of specific issues identified