Study level


Master of Philosophy


Vacation research experience scheme


Science and Engineering Faculty

School of Information Systems

Topic status

We're looking for students to study this topic.


Dr Sander Leemans
Lecturer in Business Process Management
Division / Faculty
Science and Engineering Faculty


Process mining aims to derive information from historical behaviour of processes in organisations through event logs. Business analysts use process mining software to visualise logs and derive information and insights for managers. Ultimately, this information is used to improve processes, which can optimise costs, time and/or the environment. Process mining is an exciting field with lots of opportunity for research and with many successful commercial solutions being offered.

Business process models describe what can happen in a process. That is, the order and choices between the steps (activities) that have to be executed to traverse the process and to handle a case such as an insurance claim/process an order/admit a student/etc.

In some cases, the information contained in process models is not sufficient. For instance, if you want to predict the remaining time of a particular case, information of the likelihood of future paths through the model is necessary. Furthermore, to improve processes, it might be worth focusing on the most-occuring parts of the process, rather than exceptional cases.

To solve this, stochastic process models not only express which cases are supported by a model, but also how likely each case is to occur. For instance, different types of stochastic Petri nets have been proposed, in which every transition is given a 'weight', and the 'heavier' a transition, the more likely that transition is to be fired before other competing transitions.

Several types of stochastic process models have been proposed and a discovery technique has been published.

Research activities

In this project, we aim to research several aspects of stochastic models, as well as design new techniques. Our research aims to answer the following questions:

  • How can we discover stochastic process models from event logs automatically?
  • How do we assess the quality of stochastic process models with respect to event logs or one another (conformance checking)?
  • How do we best use stochastic process models in practice?
  • How do we get the most value for industry partners (how-to manuals, methodologies, user-studies, visualisations, explanations)?
  • What are the limitations of current types of stochastic process models, and could we address them?


All of the mentioned research activities are on the edge of current scientific knowledge. Therefore, we expect to publish the performed research in top conferences and journal articles.

Skills and experience

Depending on the specific topic chosen, we expect you to have basic

  • programming skills to implement prototypes of designed techniques
  • understanding of process models, event logs, process mining and/or business process management
  • understanding of mathematics.


You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round



Contact the supervisor for more information.