On Model-Driven Optimisation of Process Digital Twins
Gabriele Taentzer
Philipps University Marburg
Digital twins are increasingly being seen in the context of business process management. Process digital twins create a digital representation of business processes that have been mined from real-world activities and their interactions. Existing business process modelling languages provide valuable input for specifying process digital twins. The optimisation of business processes is an important task to increase the efficiency of the described workflows. Metaheuristic optimisation, such as evolutionary search, has been used to optimise business process models, but it requires a high level of expertise that not all process designers have. Model-Driven Optimisation (MDO) promises to make the use of metaheuristic optimisation accessible to domain experts without deep technical expertise by allowing them to specify the optimisation algorithm directly at the model level. Optimising process digital twins can result in new process improvement opportunities. Because real-world processes can evolve during execution, there is a need to re-optimise their digital twins, which leads to new challenges in MDO.
Speaker bio: Gabriele Taentzer is a Professor at Philipps-Universität Marburg, where she leads the Software Engineering group. Her research interests include formal foundations and applications of model-driven software engineering, as well as data and software quality assurance. Her research on model-driven optimisation and its application to software engineering has been supported by the German Research Council. She was Program Co-Chair of the Int. Conference on Model-Driven Engineering Languages and Systems in 2023 and is on the editorial board of the Journal on Software and System Modeling. She is also deputy director of the Marburg Center for Digital Culture and Infrastructure, an interdisciplinary research center that focuses on digital methods in the humanities and social sciences, as well as the digital transformation of society, culture and science.