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Visualising Stochastic Business Process Models

Science and Engineering Faculty student topic

Study level

PhD

Master of Philosophy

Honours

Vacation research experience scheme

Faculty/Lead unit

Science and Engineering Faculty

School of Information Systems

Topic status

We're looking for students to study this topic.

Supervisors

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

Overview

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.

Research activities

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.).

Outcomes

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).

Keywords

Contact

Contact the supervisor for more information.