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A lightweight ontology for enterprise architecture mining of API gateway logs

2024-09-09Journal article Restricted access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorPinheiro, Carlos Roberto
dc.contributor.authorGuerreiro, Sérgio Luís P. Duarte
dc.contributor.authorSão Mamede, Henrique
dc.date.accessioned2025-02-05T16:32:39Z
dc.date.available2025-02-05T16:32:39Z
dc.date.issued2024-09-09
dc.description.abstractEnterprise Architecture (EA) is defined as a set of principles, methods, and models that support the design of organizational structures, expressing the different concerns of a company and its IT landscape, including processes, services, applications, and data. One role of EA management is to automate modeling tasks and maintain up-to-date EA models while reality changes. However, EA modeling still relies primarily on manual methods. Contributing to EA modeling automation, EA Mining is an approach that uses data mining techniques for EA modeling and management. It automatically captures existing information in operational databases to generate architectural models and views. This paper presents an ontology for EA Mining that focuses on generating architectural models from API gateway log files. An ontology defines the concepts and relationships among them to uniquely describe a domain of interest and specify the meaning of the terms. API Gateways are information technology components that serve as a facade between information systems and enterprise business partners. The ontology development methodology followed the SABiO process, whereas the Unified Foundational Ontology provided the foundations of the ontology and OntoUML, the ontology modeling language. An experiment in an e-commerce application scenario was conducted to evaluate the theoretical feasibility and applicability of the ontology. Automatic semantic and syntactic validation tools and semi-structured expert interviews were used to confirm the desired ontology properties. This study aims to contribute to the evolution of the knowledge base of EA Management.eng
dc.description.sponsorshipINESC TEC. his work was supported by the National Funds through the Portuguese Funding Agency, Fundação para a Ciência e a Tecnologia (FCT) (https://doi.org/10.54499/UIDP/50014/2020) under Project UIDP/50014/2020.
dc.identifier.citationPinheiro, C. R., Guerreiro, S. L., & Mamede, H. S. (2024). A Lightweight Ontology for Enterprise Architecture Mining of API Gateway Logs. IEEE Access.
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.2/19474
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
dc.relation.hasversionhttps://ieeexplore.ieee.org/abstract/document/10669546
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData mining
dc.subjectEnterprise architecture management
dc.subjectEnterprise architecture mining
dc.subjectOntologies
dc.titleA lightweight ontology for enterprise architecture mining of API gateway logseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50014%2F2020/PT
oaire.citation.titleIEEE Access
oaire.citation.volume12
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isProjectOfPublication13f483ad-8866-484a-b747-6ffb367bc8fd
relation.isProjectOfPublication.latestForDiscovery13f483ad-8866-484a-b747-6ffb367bc8fd

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