Process mining aims to derive information from historical behaviour of processes in organisations which has been recorded in 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 to, for instance, optimise costs, time and/or the environment. Process mining is an exciting field with lots of opportunity for research and with many 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. Business processes are described by formalisms such as BPMN, Petri nets and Process Trees.
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 that is running, information of the likelihood of future paths through the model is necessary. Furthermore, to improve processes, it might be worthwile to focus on the most-occuring parts of the process, rather than focusing on 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.
Stochastic process models have been proposed and well-defined, but key to their end-user/analyst/manager appeal is their intuitive visualisation. Therefore, in this project, we ask you to come up with and implement an intuitive visualisation for stochastic process models. Whether we target BPMN, Petri nets, Process Trees or any other stochastic modelling formalism is up to you.
The research team is currently working on the application of stochastic process models, in terms of conformance checking, process discovery and real-life usage. For Honours, Research Masters and PhDs, in addition to the design and implementation of the visualisation itself, you can take research into either stochastic process models or their visualisation further by performing user-studies (e.g. do users actually understand the visualisation?), or performing research into the stochastic process models themselves (e.g. defining stochastic models for BPMN & process trees, discover them from recorded event logs, etc.).
As deliverables, we expect a report with a small literature review and motivation/reasoning about your ideas.
Furthermore, we expect an implementation of your idea (in any language you wish [we have quite some model-visualisation libraries available in Java]).
For Honours, Research Masters and PhDs, we -of course- expect theses at appropriate levels ;). Furthermore, we expect to take the (first steps towards) publications.
Skills and experience
- Programming in any programming or mark-up language.
- A basic understanding of process models, event logs and process mining (IFN515 or equivalent).
Contact the supervisor for more information.