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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:

  • 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 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

Course Contents

  • Introduction
    • From Internet of Events to Process Mining
    • Logs: Play-in, Play Out and Reply
    • Positioning Process Mining
  • Preliminaries
    • Process Modelling and Analysis
    • Data Mining
  • From Event Logs to Process Models
    • Getting Data
    • Process Discovery
    • Advance Process Discovery Techniques
    • Tools support
  • Beyond Process Discovery
    • Conformance Checking
    • Mining Additional Perspective
  • Putting Process Mining to Work
    • Business Activity Monitoring, KPI and Improvement
    • Blockchain Technologies
    • Sensors, Internet-of-Things (IoT) and wearable devices

Study material

Classes Scheduling

Course Slides

Reference book:

  • Process Mining: Data Science in Action by W.M.P. van der Aalst, Springer Verlag, 2016 (ISBN 978-3-662-49850-7).

Tools:

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

  1. Process Mining Challenge: (i) we will provide real data set, (ii) we will provide questions to be answered
  2. 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
  3. 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

Dates (https://didattica.unicam.it/Home.do)