Research - Process Science

About the Process Science Research Group at QUT

QUT’s Process Science group is regarded as one of the leading Process Science and BPM research groups in the world and is known for conducting rigorous and relevant research applicable to and tested in the real world. Our research is grounded in real-world problems and requirements as a direct result of strong collaborations with a well-established network of Australian organisations. Members of our grouphave authored some of the world’s leading BPM textbooks, and have been invited as keynote speakers to some of the most prestigious Process Mining and BPM conferences in the world. We cover both technical and business aspects of Process Science using conceptual-analytical and empirical research. We’re known for our involvement in workflow patterns research and the open source workflow environment YAWL.

Research Strengths

Process Data Quality

In the area of process data quality, we are known for our work in patterns-based classification of event log data quality issues, developing techniques for identifying and quantifying the occurrence of event log data quality issues, and for our Odigos framework which assists users in determining the root causes of data quality at organisation, system, and personal (user) levels. We are currently developing the PraeclarusDQ framework as an open-source software framework that will provide researchers with a vehicle where they can share solutions to common event log data quality issues.

Process Data Analysis

In the area of big process data, we are working on process mining in areas like insurance claims processing and patient flow analysis, where event logs are analysed for evidence-based process improvement. This requires sophisticated real-time analysis techniques for predictive monitoring combined with interactive, insightful visualisations. We have engaged with Australian organisations on process-mining projects in the insurance, health, retail, transport and mining sectors.

Process Modelling

We’re working on problems related to managing large collections of process models, which organisations tend to create thousands of over time. For example, process model merging and versioning, searching within process models, and refactoring of a process model collection. We continue to pursue a blended research approach merging technical and empirical strategies.

Process Querying

We’re working on process querying. Process querying studies automated methods related to filtering or manipulating repositories of models that describe observed and/or envisioned processes, and relationships between the processes. A process querying method is a technique that, given a process repository and a process query, systematically implements the query in the repository, where a process query is a (formal) instruction to manage a process repository.

Process-based Transformation

Beyond the management of transactional process performance, our researchers are working on how to improve transformational process performance based on the design and successful utilisation of process-based innovation systems. In close collaboration with corporations from the logistics, finance and professional services sectors, we are working on methods including opportunity-driven process improvement, positive process deviants and rapid process redesign methodologies.

Responsible Process Mining

Conducting process mining analyses could inadvertently produce outcomes that may have undesirable consequences, for example, violation of employees’ privacy. In line with the Responsible Data Science movement (www.responsibledatascience.org), our researchers are working on how process mining can leverage information security principals (such as confidentiality, authenticity, integrity, non-repudiation) and technologies (such as cryptography) to deliver end-to-end responsible process mining practices.

Machine Learning-based Process Models

In collaboration with CRC ORE (www.crcore.org.au), our researchers are working on how process mining can be synthesised with machine learning techniques to analyse streaming process data, such as sensors readings from devices in an ore processing plant, to profile and predict their performances. We seek to produce methodology and tools to assist users in creating high-fidelity machine learning models for various equipment in a plant/factory using real-time sensor data.

Process Automation

BPA is about automating a process with a software system that enables the passing of data between resources for action, according to the dependencies set in the underlying model. We develop and maintain the YAWL open source BPA system. YAWL is designed to be widely extensible, and thus supports a wide range of research topics. We’re currently working on flexibility and exception handling extensions, cloud-based execution of business processes, novel visualisation techniques for automation perspectives, Robotic Process Automation, and Cognitive Automation.

PhD (Information Technology) Positions

Process Science Research Group – Queensland University of Technology (QUT), Brisbane, Australia

We are looking for prospective PhD candidates to work on various topic of Process Science (Process Data Analysis – Process Mining, Process Querying, Machine Learning-based Process Models, Process Model Repair, and Responsible Process Mining) within the Process Science Research Group at the Queensland University of Technology (QUT), Brisbane, Australia. View the current topics for research.

Full three-year scholarships (including fees and living allowance) are available to suitable PhD candidates. Interested candidates with strong Information Technology, Computer Science or Mathematics backgrounds are encouraged to apply. Please get in touch with us by sending your CV (plus evidence of English Proficiency where applicable) to Professor Moe Thandar Wynn (m.wynn@qut.edu.au). Further information about the BPM discipline at QUT and our research strengths is available below. Please indicate the research area that you would like to pursue in your application.

Next steps

Please send your CV to Professor Moe Thandar Wynn (m.wynn@qut.edu.au) and indicate your preferred research topic(s). You may then be asked to undertake an entry test and an interview. We will then support you to apply for entry to PhD studies and the scholarship through QUT Annual Round.

Find out about our research projects.