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Title Digital Twin Data Management: A Comprehensive Review
ID_Doc 20183
Authors Ouedraogo E.B.; Hawbani A.; Wang X.; Liu Z.; Zhao L.; Al-Qaness M.A.A.; Alsamhi S.H.
Year 2025
Published IEEE Transactions on Big Data
DOI http://dx.doi.org/10.1109/TBDATA.2025.3533891
Abstract Digital Twins are virtual representations of physical assets and systems that rely on effective Data Management to integrate, process, and analyze diverse data sources. This article comprehensively examines Data Management challenges, architectures, techniques, and applications in the context of Digital Twins. It explores key issues such as data heterogeneity, quality assurance, scalability, security, and interoperability. The paper outlines architectural approaches like centralized, distributed, cloud-based, and blockchain solutions and Data Management techniques for modeling, integration, fusion, quality management, and visualization. Domain-specific considerations across manufacturing, smart cities, healthcare, and other sectors are discussed. Finally, open research challenges related to standards, real-time data processing, intelligent Data Management, and ethical aspects are highlighted. By synthesizing the state-of-the-art, this review serves as a valuable reference for developing robust Data Management strategies that enable Digital Twin deployments. © 2015 IEEE.
Author Keywords Data integration; data management; data quality; data security; data visualization; digital twin


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