评估数字孪生参考模型的FAIR程度，助力物联网数据管理
Assessing FAIRness of the Digital Shadow Reference Model
- 用FAIR实施画像和问卷评估模型对开放原则的符合度
- 发现模型支持丰富元数据描述但缺少全球唯一标识符
- 适合关注数据可发现性与互操作性的工业数据研究者
模型在管理物联网、工业物联网及智能物体领域海量数据与复杂性中起关键作用。数字孪生参考模型作为连接数据与元数据的基础元数据架构，其遵循FAIR原则（可发现、可访问、可互操作、可重用）至关重要，可提升数据管理效率与系统集成能力。本文基于FAIR数据原则，采用结构化评估框架对数字孪生参考模型的FAIR程度进行评估。结合FAIR实施画像（FIPs）与微型问卷，系统分析其对原则的遵循情况。结果显示，该模型在元数据丰富性与认证机制方面表现良好，但缺乏全局唯一标识符，且对不同网络标准的支持不足。研究提出具体改进方向，为提升模型的可发现性与可重用性提供实践指导，推动更高效的数据管理与共享。
原文摘要 · Abstract (English)
Models play a critical role in managing the vast amounts of data and increasing complexity found in the IoT, IIoT, and IoP domains. The Digital Shadow Reference Model, which serves as a foundational metadata schema for linking data and metadata in these environments, is an example of such a model. Ensuring FAIRness (adherence to the FAIR Principles) is critical because it improves data findability, accessibility, interoperability, and reusability, facilitating efficient data management and integration across systems. This paper presents an evaluation of the FAIRness of the Digital Shadow Reference Model using a structured evaluation framework based on the FAIR Data Principles. Using the concept of FAIR Implementation Profiles (FIPs), supplemented by a mini-questionnaire, we systematically evaluate the model's adherence to these principles. Our analysis identifies key strengths, including the model's metadata schema that supports rich descriptions and authentication techniques, and highlights areas for improvement, such as the need for globally unique identifiers and consequent support for different Web standards. The results provide actionable insights for improving the FAIRness of the model and promoting better data management and reuse. This research contributes to the field by providing a detailed assessment of the Digital Shadow Reference Model and recommending next steps to improve its FAIRness and usability.
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