Digital Twins: Modelling Languages Comparison

Abdul Wahid; Jiafeng Zhu; Stefano Mauceri; Lei Li; Minghua Liu

doi:10.1007/978-3-031-25891-6_13

Digital Twins: Modelling Languages Comparison

Abdul Wahid
, Jiafeng Zhu
, Stefano Mauceri
, Lei Li
, Minghua Liu

Computer Science

University of Galway

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

1 Citation (Scopus)

Abstract

A digital twin (DT) is a virtual representation of a physical entity that can be used to improve and automate decision-making. For instance, a DT can be used for real-time performance monitoring and optimisation. In this work, we investigate DTs from the point of view of the telecommunication industry (TI). First, we provide an introduction to the main concepts, tools, and applications related to DTs. Then, we clarify the benefits of DTs for the TI. Also, we identify the main challenges hindering widespread adoption of DTs within the TI. Finally, we argue that modelling languages are the key to overcome some of the main challenges. This provides a good starting point for discussion and further research.

Original language	English
Title of host publication	Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Revised Selected Papers
Editors	Giuseppe Nicosia, Giovanni Giuffrida, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos Pardalos, Giuseppe Di Fatta, Renato Umeton
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	169-178
Number of pages	10
ISBN (Print)	9783031258909
DOIs	https://doi.org/10.1007/978-3-031-25891-6_13
Publication status	Published - 2023
Event	8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022 - Certosa di Pontignano, Italy Duration: 18 Sep 2022 → 22 Sep 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13811 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022
Country/Territory	Italy
City	Certosa di Pontignano
Period	18/09/22 → 22/09/22

Keywords

Cyber-physical systems
Decision support systems
Digital twin
Smart maintenance
System modelling

Access to Document

10.1007/978-3-031-25891-6_13

Cite this

Wahid, A., Zhu, J., Mauceri, S., Li, L., & Liu, M. (2023). Digital Twins: Modelling Languages Comparison. In G. Nicosia, G. Giuffrida, V. Ojha, E. La Malfa, G. La Malfa, P. Pardalos, G. Di Fatta, & R. Umeton (Eds.), Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Revised Selected Papers (pp. 169-178). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13811 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-25891-6_13

Wahid, Abdul ; Zhu, Jiafeng ; Mauceri, Stefano et al. / Digital Twins : Modelling Languages Comparison. Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Revised Selected Papers. editor / Giuseppe Nicosia ; Giovanni Giuffrida ; Varun Ojha ; Emanuele La Malfa ; Gabriele La Malfa ; Panos Pardalos ; Giuseppe Di Fatta ; Renato Umeton. Springer Science and Business Media Deutschland GmbH, 2023. pp. 169-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A digital twin (DT) is a virtual representation of a physical entity that can be used to improve and automate decision-making. For instance, a DT can be used for real-time performance monitoring and optimisation. In this work, we investigate DTs from the point of view of the telecommunication industry (TI). First, we provide an introduction to the main concepts, tools, and applications related to DTs. Then, we clarify the benefits of DTs for the TI. Also, we identify the main challenges hindering widespread adoption of DTs within the TI. Finally, we argue that modelling languages are the key to overcome some of the main challenges. This provides a good starting point for discussion and further research.",

keywords = "Cyber-physical systems, Decision support systems, Digital twin, Smart maintenance, System modelling",

author = "Abdul Wahid and Jiafeng Zhu and Stefano Mauceri and Lei Li and Minghua Liu",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022 ; Conference date: 18-09-2022 Through 22-09-2022",

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editor = "Giuseppe Nicosia and Giovanni Giuffrida and Varun Ojha and \{La Malfa\}, Emanuele and \{La Malfa\}, Gabriele and Panos Pardalos and \{Di Fatta\}, Giuseppe and Renato Umeton",

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Wahid, A, Zhu, J, Mauceri, S, Li, L & Liu, M 2023, Digital Twins: Modelling Languages Comparison. in G Nicosia, G Giuffrida, V Ojha, E La Malfa, G La Malfa, P Pardalos, G Di Fatta & R Umeton (eds), Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13811 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 169-178, 8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022, Certosa di Pontignano, Italy, 18/09/22. https://doi.org/10.1007/978-3-031-25891-6_13

Digital Twins: Modelling Languages Comparison. / Wahid, Abdul; Zhu, Jiafeng; Mauceri, Stefano et al.
Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Revised Selected Papers. ed. / Giuseppe Nicosia; Giovanni Giuffrida; Varun Ojha; Emanuele La Malfa; Gabriele La Malfa; Panos Pardalos; Giuseppe Di Fatta; Renato Umeton. Springer Science and Business Media Deutschland GmbH, 2023. p. 169-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13811 LNCS).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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T1 - Digital Twins

T2 - 8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022

AU - Wahid, Abdul

AU - Zhu, Jiafeng

AU - Mauceri, Stefano

AU - Li, Lei

AU - Liu, Minghua

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N2 - A digital twin (DT) is a virtual representation of a physical entity that can be used to improve and automate decision-making. For instance, a DT can be used for real-time performance monitoring and optimisation. In this work, we investigate DTs from the point of view of the telecommunication industry (TI). First, we provide an introduction to the main concepts, tools, and applications related to DTs. Then, we clarify the benefits of DTs for the TI. Also, we identify the main challenges hindering widespread adoption of DTs within the TI. Finally, we argue that modelling languages are the key to overcome some of the main challenges. This provides a good starting point for discussion and further research.

AB - A digital twin (DT) is a virtual representation of a physical entity that can be used to improve and automate decision-making. For instance, a DT can be used for real-time performance monitoring and optimisation. In this work, we investigate DTs from the point of view of the telecommunication industry (TI). First, we provide an introduction to the main concepts, tools, and applications related to DTs. Then, we clarify the benefits of DTs for the TI. Also, we identify the main challenges hindering widespread adoption of DTs within the TI. Finally, we argue that modelling languages are the key to overcome some of the main challenges. This provides a good starting point for discussion and further research.

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KW - Smart maintenance

KW - System modelling

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AN - SCOPUS:85151062002

SN - 9783031258909

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 169

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A2 - Nicosia, Giuseppe

A2 - Giuffrida, Giovanni

A2 - Ojha, Varun

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A2 - Pardalos, Panos

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A2 - Umeton, Renato

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 18 September 2022 through 22 September 2022

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Wahid A, Zhu J, Mauceri S, Li L, Liu M. Digital Twins: Modelling Languages Comparison. In Nicosia G, Giuffrida G, Ojha V, La Malfa E, La Malfa G, Pardalos P, Di Fatta G, Umeton R, editors, Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. p. 169-178. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-25891-6_13

Digital Twins: Modelling Languages Comparison

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