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A survey on association rule mining for enterprise architecture model discovery

dc.contributor.authorPinheiro, Carlos
dc.contributor.authorGuerreiro, Sérgio
dc.contributor.authorSão Mamede, Henrique
dc.date.accessioned2024-05-22T11:43:07Z
dc.date.available2024-05-22T11:43:07Z
dc.date.issued2023-12-21
dc.description.abstractAssociation Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predic- tive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suit- able for designing organizational structures. It uses view- points derived from EA models to express different concerns about a company and its IT landscape, such as organizational hierarchies, processes, services, applica- tions, and data. EA mining is the use of DM techniques to obtain EA models. This paper presents a literature review to identify the newest and most cited ARM algorithms and techniques suitable for EA mining that focus on automating the creation of EA models from existent data in application systems and services. It systematically identifies and maps fourteen candidate algorithms into four categories useful for EA mining: (i) General Frequent Pattern Mining, (ii) High Utility Pattern Mining, (iii) Parallel Pattern Mining, and (iv) Distribute Pattern Mining. Based on that, it dis- cusses some possibilities and presents an exemplification with a prototype hypothesizing an ARM application for EA mining.pt_PT
dc.description.sponsorshipOpen access funding provided by FCT|FCCN (b-on).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPinheiro, C., Guerreiro, S., & Mamede, H. S. (2023). A Survey on Association Rule Mining for Enterprise Architecture Model Discovery. Business & Information Systems Engineering, 1-22.pt_PT
dc.identifier.doi10.1007/s12599-023-00844-5pt_PT
dc.identifier.issn2363-7005
dc.identifier.urihttp://hdl.handle.net/10400.2/16058
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Vieweg-Springer Fachmedien Wiesbaden GmbHpt_PT
dc.relationINESC TEC - Institute for Systems and Computer Engineering, Technology and Science (INESC TEC)
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectAssociation rule miningpt_PT
dc.subjectData miningpt_PT
dc.subjectEnterprise architecture miningpt_PT
dc.subjectEnterprise architecture modellingpt_PT
dc.subjectArtificial intelligencept_PT
dc.titleA survey on association rule mining for enterprise architecture model discoverypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleINESC TEC - Institute for Systems and Computer Engineering, Technology and Science (INESC TEC)
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0063%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
oaire.citation.endPage22pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleBusiness & Usiness & Information Systems Engineeringpt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSão Mamede
person.givenNameHenrique
person.identifierR-002-0P0
person.identifier.ciencia-id7F17-9DAD-C007
person.identifier.orcid0000-0002-5383-9884
person.identifier.scopus-author-id36458782500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication86fd6131-eed5-42be-9639-9466ddf680ab
relation.isAuthorOfPublication.latestForDiscovery86fd6131-eed5-42be-9639-9466ddf680ab
relation.isProjectOfPublication70de26da-3525-4150-b88a-c38d047c59c5
relation.isProjectOfPublicationdb90d70d-e43e-4cab-9cfd-9b4f4db2e7af
relation.isProjectOfPublication.latestForDiscovery70de26da-3525-4150-b88a-c38d047c59c5

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