Single-Course English 5 ECTS

Process Mining

Overall Course Objectives

The aim of the course is to enable the students to understand and create formal representation of business processes (e.g., Petri net) and to understand the purpose and potential of process mining. The students will be able to understand and analyze the differences among the state-of-the-art process mining techniques and which are the ideal application conditions for each of them. Additionally, the students will be able to formulate relevant and meaningful questions, useful to analyze behavioral aspect of the process and to test these hypotheses on event logs. Finally, students will be able to independently extract and analyze event logs with commercial and open-source process mining tools available in order to answer process-related questions and test hypotheses.

Learning Objectives

  • Explain the behavior of a Petri net (e.g., “token game”) and corresponding basic properties (e.g., deadlock-free, bounded)
  • Model a given business process using Petri nets in a correct way
  • Compare the different process mining family of techniques (e.g., control-flow discovery and conformance checking) and select which fits given scenarios
  • Interpret the quality criteria for evaluating process mining outcomes, in particular concerning control-flow discovery algorithms
  • Compare the available algorithms and tools for process mining techniques and determine which fits given problems
  • Utilize the state-of-the-art of available technologies, both commercial products and open source tools
  • Construct event logs starting from raw data recordings or as simulations of process models preserving certain properties (to be used in controlled environments)
  • Extract the control-flow of the activities recorded in an event log with commercial and open source tools
  • Calculate the conformance of an event log with respect to a given reference process model
  • Assess (business) related questions using process mining techniques
  • Explain the current trends in process mining research

Course Content

The course consists of lectures and tutorials/exercises. All theoretical aspects will be supported by practical sessions consisting of both exercises and software tests.

The topics presented are:
▪ Formalizations and graphical representations of business processes
▪ Extraction of data for process mining purposes
▪ Simulation of models for process mining purposes
▪ Algorithms for control-flow discovery
▪ 4 quality dimensions of process mining
▪ Algorithms for conformance checking

Recommended prerequisites

02105/02158/02141, Understanding of algorithms and basic data structures, together with some general computer science modeling knowledge. Experience with notions of concurrent programming is also advisable.

Teaching Method

Lectures, group work and student presentations.


See course in the course database.





13 weeks




DTU Lyngby Campus

Course code 02269
Course type Candidate
Semester start Week 35
Semester end Week 48
Days Wed 8-12

7.500,00 DKK