Context
Process mining is a discipline that uses historical data extracted from an organisation about a business process to better understand process behaviour and performance. Typically, the historical data used focuses on describing the internal moments of activity within a process. This PhD project is centred around expanding upon types of data used by process mining techniques. This expansion concentrates on exogenous data, that is, data not tied to the process’s internal workings but which expresses some measurement of the context surrounding the process. This project will create opportunities for discovering and understanding contextual or external influences on processes and behaviour.
PhD Student and Supervisory Team
Adam Banham PhD student
Dr Sander Leemans Principal supervisor
Prof. Moe Wynn
Dr Robert Andrews
Publications and Resources
- Banham, S. J. J. Leemans, M. T. Wynn, R. Andrews, xPM: A framework for process mining with exogenous data, in: Process Mining Workshops – ICPM 2021 International Workshops, Eindhoven, Netherlands, October 31 – November 4, 2021, Lecture Notes in Business Information Processing, Springer, 2021
- Exogenous Data – ProM Plugin (https://www.promtools.org/doku.php). A user-friendly tool for accessing techniques proposed by this project. Currently still in development, but an implementation can be found here.
- Reproduction repository for extracting healthcare processes – A collection of process extraction scripts to find challenging event logs for future process mining initiatives. This repository will include new processes extractions as future collaborations with clinicians, and healthcare experts occur in the future. We assume that users have access to the publicly available dataset, MIMIC-III. The repository can be found here.
