Digital Twin Data Management: A Comprehensive Review

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Big Data Pub Date : 2025-01-27 DOI:10.1109/TBDATA.2025.3533891

Ezekiel B. Ouedraogo;Ammar Hawbani;Xingfu Wang;Zhi Liu;Liang Zhao;Mohammed A. A. Al-qaness;Saeed Hamood Alsamhi

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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.

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数字孪生数据管理：全面回顾

数字孪生是物理资产和系统的虚拟表示，依赖于有效的数据管理来集成、处理和分析不同的数据源。本文全面研究了数字孪生环境中的数据管理挑战、体系结构、技术和应用程序。它探讨了数据异构、质量保证、可伸缩性、安全性和互操作性等关键问题。本文概述了体系结构方法，如集中式、分布式、基于云的和区块链解决方案，以及用于建模、集成、融合、质量管理和可视化的数据管理技术。讨论了制造业、智能城市、医疗保健和其他行业的特定领域考虑事项。最后，强调了与标准、实时数据处理、智能数据管理和伦理方面相关的开放研究挑战。通过综合最新技术，本综述为开发健壮的数据管理策略提供了有价值的参考，从而实现数字孪生部署。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Big Data Multiple-

CiteScore

11.80

自引率

2.80%

发文量

114

期刊介绍： The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.