Context
One way of understanding organisations is to look at the patterns of what they repeatedly do, and what they treat as exceptional. In process mining, models of organisations at work are automatically constructed and analysed. This project focuses on discovering and analysing stochastic models of processes in organisations, using mathematical tools such as Generalised Stochastic Petri Nets. These processes may be explicitly defined by the organisation, as in an insurance claim process, or implicit, as in a hospital emergency room. Stochastic models are powerful tools for characterising routine behaviour and performance, helping us understand and improve real-world organisations from the large data sets they produce.
PhD Student and Supervisory Team
Adam Burke PhD student
Dr Sander Leemans Principal supervisor
Prof. Moe Wynn
Publications and Resources
Burke, Adam, Leemans, Sander J.J., & Wynn, Moe Thandar (2021) Discovering Stochastic Process Models by Reduction and Abstraction. In Buchs, Didier & Carmona, Josep (Eds.) Application and Theory of Petri Nets and Concurrency: 42nd International Conference, PETRI NETS 2021, Virtual Event, June 23–25, 2021, Proceedings. Springer, Switzerland, pp. 312-336.
- A publicly available implementation can be found here.
Burke, Adam, Leemans, Sander, & Wynn, Moe Thandar (2021) Stochastic Process Discovery by Weight Estimation. In Leemans, Sander & Leopold, Henrik (Eds.) Process Mining Workshops: ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers. Springer, Switzerland, pp. 260-272.
- A publicly available implementation can be found here.
