Multi-Partner Project: A Model-Driven Engineering Framework for Federated Digital Twins of Industrial Systems (MATISSE)

Digital twins are virtual representations of real-world entities or systems. Their primary goal is to help organizations understand and predict the behaviour and properties of these entities or systems. Additionally, digital twins enhance activities such as monitoring, verification, validation, and testing. However, the inherent complexity of digital twins implies challenges throughout the systems engineering process. This notably includes design, development, and analysis phases, as well as deployment, execution, and maintenance. Moreover, existing approaches, methods, techniques, and tools for modelling, simulating, validating, and monitoring single digital twins must now address the increased complexity in federation scenarios. These scenarios introduce new challenges, such as digital twin identification, shared metadata, cross-digital twin communication and synchronization, and federation governance. The KDT Joint Undertaking MATISSE project tackles these challenges by aiming to provide a model-driven framework for the continuous engineering of federated digital twins. It leverages model-driven engineering techniques and practices as the core enabling technology, with traceability serving as an essential infrastructural service for the digital twins federation. In this paper, we introduce the MATISSE conceptual framework for digital twins, highlighting both the novelty of the project's concept and its technical objectives. As the project is still in its initial phase, we identify key research challenges relevant to the DATE community and propose a preliminary research roadmap. This roadmap addresses traceability and federation mechanisms, the required continuous engineering strategy, and the development of digital twin-based services for verification, validation, prediction, and monitoring. To illustrate our approach, we present two concrete scenarios that demonstrate practical applications of the MATISSE conceptual framework.

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Publication Details

Published
2025-03-31
DOI
https://doi.org/10.23919/date64628.2025.10993046
Citations
4
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
9.14
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Multi-Partner Project: A Model-Driven Engineering Framework for Federated Digital Twins of Industrial Systems (MATISSE)

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4 citations
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article

Multi-Partner Project: A Model-Driven Engineering Framework for Federated Digital Twins of Industrial Systems (MATISSE)

Andrey Sadovykh, Hugo Brunelière, Manuel Wimmer, Romina Eramo, Luca Berardinelli, Vittoriano Muttillo, Alessio Bucaioni, Djamel Eddine Khelladi, Benôıt Combemale
article en
4 citations

Abstract

Digital twins are virtual representations of real-world entities or systems. Their primary goal is to help organizations understand and predict the behaviour and properties of these entities or systems. Additionally, digital twins enhance activities such as monitoring, verification, validation, and testing. However, the inherent complexity of digital twins implies challenges throughout the systems engineering process. This notably includes design, development, and analysis phases, as well as deployment, execution, and maintenance. Moreover, existing approaches, methods, techniques, and tools for modelling, simulating, validating, and monitoring single digital twins must now address the increased complexity in federation scenarios. These scenarios introduce new challenges, such as digital twin identification, shared metadata, cross-digital twin communication and synchronization, and federation governance. The KDT Joint Undertaking MATISSE project tackles these challenges by aiming to provide a model-driven framework for the continuous engineering of federated digital twins. It leverages model-driven engineering techniques and practices as the core enabling technology, with traceability serving as an essential infrastructural service for the digital twins federation. In this paper, we introduce the MATISSE conceptual framework for digital twins, highlighting both the novelty of the project's concept and its technical objectives. As the project is still in its initial phase, we identify key research challenges relevant to the DATE community and propose a preliminary research roadmap. This roadmap addresses traceability and federation mechanisms, the required continuous engineering strategy, and the development of digital twin-based services for verification, validation, prediction, and monitoring. To illustrate our approach, we present two concrete scenarios that demonstrate practical applications of the MATISSE conceptual framework.

Johannes Kepler University of Linz (AT), University of Teramo (IT), Centre National de la Recherche Scientifique (FR), Softeam Cadextan (FR), Laboratoire des Sciences du Numérique de Nantes (FR), IMT Atlantique (FR), Université de Rennes (FR), Nantes Université (FR)
Industry, innovation and infrastructure
Openalex Percentile: Top 3%
Business Process Modeling and Analysis
9.14
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