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2024 | OriginalPaper | Chapter

DMFDT: Data Management Framework for Digital Twin

Authors : Zeeshan Ali, Milad Poursoltan, Mamadou Kaba Traore

Published in: Navigating Unpredictability: Collaborative Networks in Non-linear Worlds

Publisher: Springer Nature Switzerland

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Abstract

Digital Twin (DT) provides a digital representation of a real-world entity (process or product) that is continuously synchronized with a specified frequency. In this regard, DT utilizes a set of models that capture the various aspects of the real system to provide a deeper understanding and analysis of its real counterpart. The data within the DT holds paramount significance and serves as the foundation for model updating, refining, interoperability, validity, usability, etc. Accordingly, DT requires rigorous data management throughout its entire life cycle. This paper explores data knowledge areas related to DT (i.e., data governance, architecture, modeling, integration, interoperability, quality, uncertainty, visualization, and security) and also highlights their best practices, and proposes a Data Management Framework for Digital Twin (DMFDT) to facilitate a better understanding of the DT data related requirements and proven practices. Validation and application of the DMFDT is done through the high-level DT architecture and a case study of the proposed framework is also presented by a DT developed to study the mobility system at the University of Bordeaux in France.

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Metadata
Title
DMFDT: Data Management Framework for Digital Twin
Authors
Zeeshan Ali
Milad Poursoltan
Mamadou Kaba Traore
Copyright Year
2024
DOI
https://doi.org/10.1007/978-3-031-71743-7_9

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