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A framework for interoperability between models with hybrid tools

Complex system development and maintenance face the challenge of dealing with different types of models due to language affordances, preferences, sizes, and so forth that involve interaction between users with different levels of proficiency. Current conceptual data modelling tools do not fully supp...

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Detalles Bibliográficos
Autores principales: Braun, Germán, Fillottrani, Pablo Rubén, Keet, C. Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334976/
https://www.ncbi.nlm.nih.gov/pubmed/35919102
http://dx.doi.org/10.1007/s10844-022-00731-7
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author Braun, Germán
Fillottrani, Pablo Rubén
Keet, C. Maria
author_facet Braun, Germán
Fillottrani, Pablo Rubén
Keet, C. Maria
author_sort Braun, Germán
collection PubMed
description Complex system development and maintenance face the challenge of dealing with different types of models due to language affordances, preferences, sizes, and so forth that involve interaction between users with different levels of proficiency. Current conceptual data modelling tools do not fully support these modes of working. It requires that the interaction between multiple models in multiple languages is clearly specified to ensure they keep their intended semantics, which is lacking in extant tools. The key objective is to devise a mechanism to support semantic interoperability in hybrid tools for multi-modal modelling in a plurality of paradigms, all within one system. We propose FaCIL, a framework for such hybrid modelling tools. We design and realise the framework FaCIL, which maps UML, ER and ORM2 into a common metamodel with rules that provide the central point for management among the models and that links to the formalisation and logic-based automated reasoning. FaCIL supports the ability to represent models in different formats while preserving their semantics, and several editing workflows are supported within the framework. It has a clear separation of concerns for typical conceptual modelling activities in an interoperable and extensible way. FaCIL structures and facilitates the interaction between visual and textual conceptual models, their formal specifications, and abstractions as well as tracking and propagating updates across all the representations. FaCIL is compared against the requirements, implemented in crowd 2.0, and assessed with a use case. The proof-of-concept implementation in the web-based modelling tool crowd 2.0 demonstrates its viability. The framework also meets the requirements and fully supports the use case.
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spelling pubmed-93349762022-07-29 A framework for interoperability between models with hybrid tools Braun, Germán Fillottrani, Pablo Rubén Keet, C. Maria J Intell Inf Syst Article Complex system development and maintenance face the challenge of dealing with different types of models due to language affordances, preferences, sizes, and so forth that involve interaction between users with different levels of proficiency. Current conceptual data modelling tools do not fully support these modes of working. It requires that the interaction between multiple models in multiple languages is clearly specified to ensure they keep their intended semantics, which is lacking in extant tools. The key objective is to devise a mechanism to support semantic interoperability in hybrid tools for multi-modal modelling in a plurality of paradigms, all within one system. We propose FaCIL, a framework for such hybrid modelling tools. We design and realise the framework FaCIL, which maps UML, ER and ORM2 into a common metamodel with rules that provide the central point for management among the models and that links to the formalisation and logic-based automated reasoning. FaCIL supports the ability to represent models in different formats while preserving their semantics, and several editing workflows are supported within the framework. It has a clear separation of concerns for typical conceptual modelling activities in an interoperable and extensible way. FaCIL structures and facilitates the interaction between visual and textual conceptual models, their formal specifications, and abstractions as well as tracking and propagating updates across all the representations. FaCIL is compared against the requirements, implemented in crowd 2.0, and assessed with a use case. The proof-of-concept implementation in the web-based modelling tool crowd 2.0 demonstrates its viability. The framework also meets the requirements and fully supports the use case. Springer US 2022-07-29 2023 /pmc/articles/PMC9334976/ /pubmed/35919102 http://dx.doi.org/10.1007/s10844-022-00731-7 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Braun, Germán
Fillottrani, Pablo Rubén
Keet, C. Maria
A framework for interoperability between models with hybrid tools
title A framework for interoperability between models with hybrid tools
title_full A framework for interoperability between models with hybrid tools
title_fullStr A framework for interoperability between models with hybrid tools
title_full_unstemmed A framework for interoperability between models with hybrid tools
title_short A framework for interoperability between models with hybrid tools
title_sort framework for interoperability between models with hybrid tools
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334976/
https://www.ncbi.nlm.nih.gov/pubmed/35919102
http://dx.doi.org/10.1007/s10844-022-00731-7
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