Description
The AutoTwin project addresses the technological shortcoming and economic liability of the development and usage of digital twins that are accepted as the accelerator and enabler of Circular Economy in businesses and production by conduction research in 3 areas:
- introducing a breakthrough method for automated process-aware discovery towards autonomous Digital Twins generation, to support trustworthy business processes in circular economies;
- adopting an (International Data Space) IDS- based common data space, to promote and facilitate the secure and seamless exchange of manufacturing/product/business data within value-networks in a circular-economy ecosystem;
- integrating novel hardware technologies into the digital thread, to create smart Green Gateways, empowering companies to perform data and digital twin enabled green decisions, and to unleash their full potential for actual zero-waste Circular Economy and reduced dependency from raw materials.
There are 13 partners from 8 different countries in the project that will be coordinated by Politecnico di Milano.
Links
Publications
- Implementing Object-Centric Event Data Models in Event Knowledge Graphs - Swevels, A., Fahland, D., & Montali, M. (2024). Implementing Object-Centric Event Data Models in Event Knowledge Graphs. In J. De Smedt, & P. Soffer (Eds.), Process Mining Workshops – ICPM 2023 International Workshops, 2023, Revised Selected Papers (pp. 431-443). (Lecture Notes in Business Information Processing; Vol. 503 LNBIP). https://doi.org/10.1007/978-3-031-56107-8_33 Abstract Recent advances in object-centric process Read More ...
- How well can large language models explain business processes? - Fahland, D., Fournier, F., Limonad, L., Skarbovsky, I., & Swevels, A. J. E. (2024). How well can large language models explain business processes? arXiv, abs/2401.12846. https://doi.org/10.48550/arXiv.2401.12846 Abstract Large Language Models (LLMs) are likely to play a prominent role in future AI-augmented business process management systems (ABPMSs) catering functionalities across all system lifecycle stages. One such Read More ...
Staff
- Dirk Fahland - Dirk is Associate Professor (UHD) in the PA group. He completed his PhD with summa cum laude at Humboldt-Univeristät zu Berlin and Eindhoven University of Technology in 2010. His research interests include distributed processes and systems built from distributed components for which he investigates modeling systems (using process modeling languages, Petri nets, or scenario-based techniques), Read More ...
- Eric Verbeek - Eric is the scientific programmer in the PA group. As such, he is the custodian of the process mining framework ProM. In you want access to the ProM repository, or have any questions related to ProM and its development, ask Eric. Recently, he has been working on a decomposition framework for both process discovery as Read More ...
- Ava Swevels - Position: PhD Student Room: MF 7.060 Tel (internal): Links: CoursesPresentationsProjectsPublications External links: Scopus pageORCID pageDBLP pageTU/e page Recent courses Recent presentations Recent projects Recent publications
- Francesca Zerbato - Position: UD Room: MF 7.061 Tel (internal): Links: External links: Google Scholar pageScopus pageTU/e page Francesca Zerbato received her Ph.D. from the Department of Computer Science at the University of Verona (Italy). Her thesis focused on the modeling of temporal aspects and data in business process models under the supervision of Prof. Carlo Combi. After Read More ...
- Abd Alrhman Abu Sbeit - Abd Alrhman Abu Sbeit is a scientific engineer at Eindhoven University of Technology – Data Science Domain – Process Analytics cluster. He works under the supervision of Dirk Fahland. Position: Scientific engineer Room: MF 7.117 Tel (internal): Links: External links: Recent courses Recent presentations Recent projects Recent publications