Incorporation of machine learning in the selection of event patterns for Smart Contract Modelling

Autores
Medina, Oscar Carlos; Meloni, Brenda Elizabeth; Strub, Ana María; Marciszack, Marcelo Martín
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Event patterns are used to model interactions between smart contracts and their environment, improving the efficiency, security, and flexibility of applications based on blockchain technologies. Event pattern-driven smart contract modelling is an emerging trend. Defining a new pattern or selecting a pre-existing one from a limited catalog is a fundamental activity for any analysis model that utilizes event patterns. This study presents a proposal to manage a catalog of event patterns that can be applied to the conceptual modelling of smart contracts and incorporates machine learning techniques to optimize pattern selection. To test this proposal, a prototype application called PatCat (Pattern Catalogue) was developed, using a decentralized electronic voting application as a case study. The incorporation of patterns at the beginning of the modelling process simplifies and clarifies the elicitation of requirements, among other benefits, while the use of machine learning accelerates the description of the problem situation. Consequently, a specialized application for managing a catalog of event patterns, supported by machine learning techniques, proves useful in standardizing and streamlining smart contract modelling tasks.
Fil: Medina, Oscar Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.
Fil: Meloni, Brenda Elizabeth. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.
Fil: Strub, Ana María. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.
Fil: Marciszack, Marcelo Martín. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.
Peer Reviewed
Fuente
Https://dspace.palermo.edu/ojs/index.php/cyt/article/view/12490/22769
Materia
Machine Learning
Smart contract
Blockchain
Artificial Intelligence
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Attribution-NonCommercial-NoDerivs 2.5 Argentina
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/15156
Acceder
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Incorporation of machine learning in the selection of event patterns for Smart Contract ModellingMedina, Oscar CarlosMeloni, Brenda ElizabethStrub, Ana MaríaMarciszack, Marcelo MartínMachine LearningSmart contractBlockchainArtificial IntelligenceEvent patterns are used to model interactions between smart contracts and their environment, improving the efficiency, security, and flexibility of applications based on blockchain technologies. Event pattern-driven smart contract modelling is an emerging trend. Defining a new pattern or selecting a pre-existing one from a limited catalog is a fundamental activity for any analysis model that utilizes event patterns. This study presents a proposal to manage a catalog of event patterns that can be applied to the conceptual modelling of smart contracts and incorporates machine learning techniques to optimize pattern selection. To test this proposal, a prototype application called PatCat (Pattern Catalogue) was developed, using a decentralized electronic voting application as a case study. The incorporation of patterns at the beginning of the modelling process simplifies and clarifies the elicitation of requirements, among other benefits, while the use of machine learning accelerates the description of the problem situation. Consequently, a specialized application for managing a catalog of event patterns, supported by machine learning techniques, proves useful in standardizing and streamlining smart contract modelling tasks.Fil: Medina, Oscar Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.Fil: Meloni, Brenda Elizabeth. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.Fil: Strub, Ana María. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.Fil: Marciszack, Marcelo Martín. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.Peer ReviewedUniversidad de Palermo2026-06-11T18:21:36Z2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfhttps://hdl.handle.net/20.500.12272/15156Https://dspace.palermo.edu/ojs/index.php/cyt/article/view/12490/22769reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacionalenginfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 2.5 Argentinahttp://creativecommons.org/licenses/by-nc-nd/2.5/ar/Medina, Oscar Carlos; Meloni, Brenda Elizabeth; Strub, Ana María; Marciszack, Marcelo Martínhttps://creativecommons.org/licenses/by-nc-nd/4.0/2026-10-01T11:59:39Zoai:ria.utn.edu.ar:20.500.12272/15156instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-10-01 11:59:40.378Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Incorporation of machine learning in the selection of event patterns for Smart Contract Modelling
title Incorporation of machine learning in the selection of event patterns for Smart Contract Modelling
spellingShingle Incorporation of machine learning in the selection of event patterns for Smart Contract Modelling
Medina, Oscar Carlos
Machine Learning
Smart contract
Blockchain
Artificial Intelligence
title_short Incorporation of machine learning in the selection of event patterns for Smart Contract Modelling
title_full Incorporation of machine learning in the selection of event patterns for Smart Contract Modelling
title_fullStr Incorporation of machine learning in the selection of event patterns for Smart Contract Modelling
title_full_unstemmed Incorporation of machine learning in the selection of event patterns for Smart Contract Modelling
title_sort Incorporation of machine learning in the selection of event patterns for Smart Contract Modelling
dc.creator.none.fl_str_mv Medina, Oscar Carlos
Meloni, Brenda Elizabeth
Strub, Ana María
Marciszack, Marcelo Martín
author Medina, Oscar Carlos
author_facet Medina, Oscar Carlos
Meloni, Brenda Elizabeth
Strub, Ana María
Marciszack, Marcelo Martín
author_role author
author2 Meloni, Brenda Elizabeth
Strub, Ana María
Marciszack, Marcelo Martín
author2_role author
author
author
dc.subject.none.fl_str_mv Machine Learning
Smart contract
Blockchain
Artificial Intelligence
topic Machine Learning
Smart contract
Blockchain
Artificial Intelligence
dc.description.none.fl_txt_mv Event patterns are used to model interactions between smart contracts and their environment, improving the efficiency, security, and flexibility of applications based on blockchain technologies. Event pattern-driven smart contract modelling is an emerging trend. Defining a new pattern or selecting a pre-existing one from a limited catalog is a fundamental activity for any analysis model that utilizes event patterns. This study presents a proposal to manage a catalog of event patterns that can be applied to the conceptual modelling of smart contracts and incorporates machine learning techniques to optimize pattern selection. To test this proposal, a prototype application called PatCat (Pattern Catalogue) was developed, using a decentralized electronic voting application as a case study. The incorporation of patterns at the beginning of the modelling process simplifies and clarifies the elicitation of requirements, among other benefits, while the use of machine learning accelerates the description of the problem situation. Consequently, a specialized application for managing a catalog of event patterns, supported by machine learning techniques, proves useful in standardizing and streamlining smart contract modelling tasks.
Fil: Medina, Oscar Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.
Fil: Meloni, Brenda Elizabeth. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.
Fil: Strub, Ana María. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.
Fil: Marciszack, Marcelo Martín. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Ingeniería en Sistemas de Información; Argentina.
Peer Reviewed
description Event patterns are used to model interactions between smart contracts and their environment, improving the efficiency, security, and flexibility of applications based on blockchain technologies. Event pattern-driven smart contract modelling is an emerging trend. Defining a new pattern or selecting a pre-existing one from a limited catalog is a fundamental activity for any analysis model that utilizes event patterns. This study presents a proposal to manage a catalog of event patterns that can be applied to the conceptual modelling of smart contracts and incorporates machine learning techniques to optimize pattern selection. To test this proposal, a prototype application called PatCat (Pattern Catalogue) was developed, using a decentralized electronic voting application as a case study. The incorporation of patterns at the beginning of the modelling process simplifies and clarifies the elicitation of requirements, among other benefits, while the use of machine learning accelerates the description of the problem situation. Consequently, a specialized application for managing a catalog of event patterns, supported by machine learning techniques, proves useful in standardizing and streamlining smart contract modelling tasks.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026-06-11T18:21:36Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12272/15156
url https://hdl.handle.net/20.500.12272/15156
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 2.5 Argentina
http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Medina, Oscar Carlos; Meloni, Brenda Elizabeth; Strub, Ana María; Marciszack, Marcelo Martín
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 2.5 Argentina
http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Medina, Oscar Carlos; Meloni, Brenda Elizabeth; Strub, Ana María; Marciszack, Marcelo Martín
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv Universidad de Palermo
publisher.none.fl_str_mv Universidad de Palermo
dc.source.none.fl_str_mv Https://dspace.palermo.edu/ojs/index.php/cyt/article/view/12490/22769
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.364332

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