Modern predictive analytics, underpinned by AI-enabled learning (such as machine learning, deep learning) techniques, has become a key enabler to the automation of data-driven decision making.
In the context of business process management, predictive analytics makes predictions about the future state of a running business process instance. These predictions can include:
- which task will be carried out next
- when the task be carried out
- who will perform the task
- when an ongoing process instance will be complete
- what the outcome will be upon completion.
Machine learning models can be trained on event log data, by recording historical process execution, to build the underlying predictive models.
Multiple techniques have been proposed so far which encode the information available in an event log and construct input features required to train a predictive model.
While accuracy has been a dominant criterion in the choice of various techniques, these techniques are often applied as a black-box in building predictive models.
In this project, we aim to develop new methods and techniques to (machine) learn from event log data. This will be used to generate accurate and explainable predictions about the future state of ongoing business process execution. These predictions can be used to provide transparent, trustful and human-understandable insights for decision making.
For more information about this research project, check out our recent publication on arXiv.
The following research activities can be scoped to cater for different types of research student projects:
- Designing and implementing models and algorithms for generating process predictions and prediction interpretations (e.g., the reasoning behind predictions).
- Designing and implementing methods and techniques for generating user-centric explanations for process predictions.
- Developing and testing an open-source framework for realising the proposed design and rendering the results to users in an understandable, visual and interactive manner.
Upon conclusion of this research project, we expect to have new or improved:
- models and algorithms for generating process predictions and prediction interpretations
- methods and techniques for generating user-centric explanations for process predictions
- tools and visualisation of explainable AI-enabled predictive analytics to support human decision-making.
Skills and experience
To be considered for this project, we expect you to have:
- knowledge of data mining and machine learning
- knowledge of process mining/analytics (preferable)
- programming skills (preferably in Python)
- problem-solving and critical thinking skills
- reasonable writing skills.
You may be able to apply for a research scholarship in our annual scholarship round.
Contact the supervisor for more information.