Abstract
Digital twins (DTs) are highly accurate virtual replicas of physical objects that enhance functionality through simulation, prediction, and analysis capabilities. Constructing DT can be considered a collaborative process between experts who need to integrate different simulation models to create cohesive services. However, challenges arise in comprehending the functionalities of these models, understanding how to use and reuse them, and effectively transferring knowledge for collaborative purposes. This paper lays the foundation for a collaborative model-based approach to addressing these challenges and enhancing comprehension of simulation models and knowledge sharing. This is achieved through a metamodel that represents the knowledge base and a detailed procedure designed to enhance model knowledge assimilation and sharing within a collaborative process.
Original language | English |
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Title of host publication | MODELS Companion '24 |
Subtitle of host publication | Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Pages | 660-664 |
Number of pages | 5 |
ISBN (Electronic) | 979-8-4007-0622-6 |
DOIs | |
Publication status | Published - 31 Oct 2024 |
Event | 27th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2024 - Linz, Austria Duration: 22 Sept 2024 → 27 Sept 2024 |
Conference
Conference | 27th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2024 |
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Abbreviated title | MODELS 2024 |
Country/Territory | Austria |
City | Linz |
Period | 22/09/24 → 27/09/24 |
Keywords
- digital twins
- collaborative knowledge transfer
- model-based approach
- simulation models