Learning objectives & syllabus

Learning objectives

By completing this course learners 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.


  • Introduction, Process Modeling and Analysis: Process mining is introduced and key concepts explored. In this topic students learn about event logs, the input for process mining, and about Petri nets, the process modelling notation used to explain foundational concepts. This topic also provides the theoretical foundations of process modelling and process discovery. In the next three topics, students will use these concepts in a more applied setting.
  • From Event Logs to Process Models: In this topic, a practical aspect of process mining is introduced. Students learn basic discovery algorithms (i.e. alpha-algorithm) to discover models from event logs. Students are introduced to the process of turning data (from various data sources) into proper event logs, needed for process mining. Moreover, challenges encountered with event logs such as noise and incompleteness are discussed.
  • Advanced Process Discovery Techniques: In this topic students learn even more process discovery algorithms. In the first part, the main focus is on conformance checking, i.e., aligning observed behaviour with modelled behaviour. This can be used for a wide variety of compliance questions: Where and why do people, machines, and organisations deviate? Students learn different ways of evaluating the conformance between a process model and the event log. The second part of lectures during this topic explores different perspectives that can also be mined from event logs. Techniques for social network analysis, resource behaviour and decision point are discussed. 
  • Putting Process Mining to Work: In this last topic, the emphasis in on the application of process mining to real life use cases. We demonstrate how to conduct a process mining project from start to finish.  We also discuss ProM and other available process mining tools that can be used to perform experiments.

Last modified: Thursday, 3 December 2015, 9:19 AM