Supporting Energy Digital Twins with Cloud Data Spaces: An Architectural Proposal

Rucco, Chiara; Longo, Antonella; Zappatore, Marco

doi:10.1007/978-3-031-13324-4_5

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Other approaches advance research in the field of data space in the meaning of the communication of data from different providers or places and how the data is collected, stored, processed, and consumed. In Reference 24, the authors propose an architecture that, following the FAIR principles, process and analyze data from different IoT sources to create a system capable of searching the information about the energy information of Italy. Another paper that focuses on data space is, 25 where the authors propose an architecture, following the FAIR principles, for integrating the data from different providers by applying techniques such as machine learning and a federation approach.…”

Section: Literature Reviewmentioning

confidence: 99%

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

Llopis,

Fernández‐García,

Criado

et al. 2024

Softw Pract Exp

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The W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability.

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Section: Literature Reviewmentioning

confidence: 99%

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

Llopis,

Fernández‐García,

Criado

et al. 2024

Softw Pract Exp

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Digital Twin Architecture for a Sustainable Control System in Aircraft Engines

Farsi,

Namoano,

Latsou

et al. 2024

Sustainable Aviation

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Digital Twins for Research and Innovation in Support of the European Green Deal Data Space: A Systematic Review

Otsu,

Maso

2024

Remote Sensing

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According to the European Data Strategy established in 2020, the European Open Science Cloud (EOSC) is described as “the basis for a science, research and innovation data space… and will be connected and articulated with the sectoral data spaces”, being one of the 14 European Common Data Spaces. While current cross-sectoral interactions with the EOSC are realised through the association’s Health Data Task Force, advancements in other EU priorities, such as the Green Deal and the Digital Strategy, should be accelerated in the green and digital transitions and integrated by reinforcing each other to be climate-neutral by 2050. With this motivation, this paper systematically reviews data spaces and digital twins (DTs) within the context of research and innovation. Specifically, focusing on the relevance of the EOSC to the Green Deal Data Space (GDDS) and DTs of the Earth, the relationship between them is explored using a topic search with various keyword combinations in the Web of Science and CORDIS databases. Based on the selected scientific articles and projects, collaboration opportunities are mapped to connect relevant stakeholders. Furthermore, existing and developing service components that could contribute to technical building blocks for the GDDS are identified. In summary, key findings are highlighted, addressing the current gaps and opportunities among the GDDS initiatives presented in this review.

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Supporting Energy Digital Twins with Cloud Data Spaces: An Architectural Proposal

Cited by 3 publications

References 14 publications

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

Digital Twin Architecture for a Sustainable Control System in Aircraft Engines

Digital Twins for Research and Innovation in Support of the European Green Deal Data Space: A Systematic Review

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