TY - GEN
T1 - Models Meet Data
T2 - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021
AU - Van Den Brand, Mark
AU - Cleophas, Loek
AU - Gunasekaran, Raghavendran
AU - Haverkort, Boudewijn
AU - Negrin, David A.Manrique
AU - Muctadir, Hossain Muhammad
N1 - Funding Information:
This research was funded by NWO (the Dutch national research council) under the NWO AES Perspectief program, project code P18-03 P3.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In recent years, digital twin (DT) technology has moved to the center of attention of many researchers and engineers. Commonly, a digital twin is defined based on a virtual entity (VE) that exhibits similar behavior to its physical counterpart, and that is coupled to this physical entity (PE). The VE thus forms a core part of any digital twin. While VEs may differ vastly - from ones based on simple simulation to high-fidelity virtual mirroring of the corresponding PE - they are typically composed of multiple models that may originate from multiple domains, address different aspects, and are expressed and processed using different tools and languages. Furthermore, the use of time series data - whether historical or real-time or both - from the PE distinguishes VEs from mere simulations. As a consequence of the modeling landscape complexity and the data aspect of VEs, the design of a digital twin and specifically of the VE as part of it represents several challenges. In this paper, we present our vision for the development, evolution, maintenance, and verification of such virtual entities for digital twins.
AB - In recent years, digital twin (DT) technology has moved to the center of attention of many researchers and engineers. Commonly, a digital twin is defined based on a virtual entity (VE) that exhibits similar behavior to its physical counterpart, and that is coupled to this physical entity (PE). The VE thus forms a core part of any digital twin. While VEs may differ vastly - from ones based on simple simulation to high-fidelity virtual mirroring of the corresponding PE - they are typically composed of multiple models that may originate from multiple domains, address different aspects, and are expressed and processed using different tools and languages. Furthermore, the use of time series data - whether historical or real-time or both - from the PE distinguishes VEs from mere simulations. As a consequence of the modeling landscape complexity and the data aspect of VEs, the design of a digital twin and specifically of the VE as part of it represents several challenges. In this paper, we present our vision for the development, evolution, maintenance, and verification of such virtual entities for digital twins.
KW - Digital twin
KW - Digital twin development roadmap
KW - Dynamic consistency
KW - Model consistency
KW - Model management
KW - Model orchestration
UR - http://www.scopus.com/inward/record.url?scp=85124001119&partnerID=8YFLogxK
U2 - 10.1109/MODELS-C53483.2021.00039
DO - 10.1109/MODELS-C53483.2021.00039
M3 - Conference contribution
AN - SCOPUS:85124001119
T3 - Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021
SP - 225
EP - 228
BT - Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 October 2021 through 15 October 2021
ER -