Academic stories: Moe Thandar Wynn

OCED XES academic stories

Narrated by Moe Wynn

My name is Moe Thandar Wynn, and I lead the Process Science Academic Program – formerly the Business Process Management (BPM) Group – at Queensland University of Technology (QUT), Brisbane, Australia. My team consists of 16 members (8 researchers and 8 HDR students), and we conduct research on process mining, data governance, (robotic) process automation, and holistic BPM. I also co-lead the Data for Discovery Program within QUT’s Centre for Data Science. I have recently been appointed as a member of the Australian Research Council College of Experts (2023 – 2025).

I was awarded my PhD in workflow management in February 2007 from QUT under the supervision of Dr David Edmond, Professor Arthur ter Hofstede, and Professor Wil van der Aalst. My thesis contributed to the formal aspects of process automation and verification, and provided insights into the execution semantics of the OR-join, paving the uptake of this construct in the BPM field. I also developed efficient techniques for design-time analysis to determine the correctness of workflows using reset nets (a special type of Petri net that supports the notion of cancellation). Although the verification problem of large complex workflows is ‘undecidable’ in general, my approach allows such models to be checked automatically for correctness in many practical settings. I worked closely with Eric Verbeek to design (too many!) reduction rules for reset nets and YAWL-nets. I implemented the OR-join semantics and the verification functionality in the open-source workflow framework, YAWL.

Over the last 15 years, my emphasis in the field of BPM has shifted from process automation (the topic of my PhD) to process mining. My first foray into process mining was during my post-doc years, where I worked with Anne Rozinat, Arthur, and Wil to design a simulation system for operational decision support. Our approach used event logs from a workflow system as input, and we demonstrated its capabilities using the YAWL system, the ProM framework, and the CPN Tools. I also led the work on cost-aware BPM, developed cost-informed process improvement and mining techniques, and proposed a cost extension to the IEEE XES standard. Other bodies of work revolve around comparative process analytics (where a visualisation technique for process comparisons was developed), resource profiling (Anastasiia Pika’s PhD), gamification and visual analytics for activity label repair (Sareh Sadeghianasl’s PhD) and the critical success factors and impact factors for process mining (Azumah Mamudu’s PhD).  I have over fifteen years of industry-informed research experience across multiple Australian sectors engaging with logistics, healthcare, insurance, utility, education, government, mining, and agri-food supply chains to pinpoint process inefficiencies and derive data-driven improvements. 

My ongoing research interests include process-data quality, healthcare process analytics, and prescriptive process mining. My team at QUT, together with several international collaborators, have been working on a range of process-data quality related topics, including event log imperfections patterns, root-cause analysis, quality-informed process mining, and an open-source process data quality framework called PraeclarusPDQ. For instance, the quality-informed process mining paper together with Kanika Goel, Sander Leemans, and Niels Martin showed how data quality annotations can be used as part of the “Quality-Informed visual Miner” plug-in. We have also expanded the event log imperfections patterns (co-authored with Suriadi, Robert Andrews and Arthur) to study the imperfection patterns specific to timestamps, digital health data and object-centric data. I also support the efforts of the Process-Oriented Data Science for Healthcare Alliance to advance the research on techniques tailored towards process mining in healthcare and contribute my experience working with healthcare data from Australian hospitals. I am working together with Maximilian Röglinger and his team on prescriptive analytics techniques for process and resource redesign with tool support for assisted process redesign

Over the last three years, I have been involved in the efforts by the IEEE Taskforce on Process Mining to re-standardise the IEEE XES standard and to gain consensus on an object-centric meta-model as the input format for Object-centric Process Mining: the Object-Centric Event Data (OCED) standard.  

Reflecting on my academic career journey of 20+ years, one of the highlights for me is the people that I get to know well: long-standing collaborators such as Arthur ter Hofstede, Wil van der Aalst, Hajo Reijers, Maximilian Röglinger, past and present QUT team members, and many collaborators from the international BPM and Process Mining communities. The work is much more enjoyable when you like the people that you work with! (And this makes my late evening online meetings with European collaborators bearable :)