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Data Engineering Roadmap for Implementing Business Intelligence in Higher Education

2024Conference paper Open access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg07:Energias Renováveis e Acessíveis
dc.contributor.authorSequeira, Romeu
dc.contributor.authorReis, Arsénio
dc.contributor.authorBranco, Frederico
dc.contributor.authorAlves, Paulo
dc.date.accessioned2025-03-17T12:03:44Z
dc.date.available2025-03-17T12:03:44Z
dc.date.issued2024
dc.description.abstractThis article addresses the implementation of Business Intelligence (BI) systems in Higher Education Institutions (HEIs), focusing on developing an appropriate data architecture that meets the specificities and requirements of this sector. With the rapid advance of information technologies, HEIs face the growing challenge of managing a considerable volume of data, making it essential to implement BI systems that support informed and efficient decision-making. Using the Design Science Research methodology, this study proposes a BI architecture model that aligns technologies with HEIs' academic and administrative needs and facilitates their integration and ongoing maintenance. The model is designed to be flexible and scalable, allowing adaptations as institutional needs evolve. The article describes the architecture development process, from initial planning to implementation, and discusses how this framework can significantly improve data management and the quality of decision-making processes in educational institutions. The research offers practical and theoretical insights for academics and managers seeking to optimize the use of BI in educational contexts.eng
dc.description.sponsorshipThis work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. The authors acknowledge the work facilities and equipment provided by CeDRI (UIDB/05757/2020 and UIDP/05757/2020) to the project team.
dc.identifier.citationSequeira, Romeu; Reis, Arsénio; Branco, Frederico; Alves, Paulo (2024). Data Engineering Roadmap for Implementing Business Intelligence in Higher Education. In 9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on Computer Science and Information Systems 2024, MCCSIS 2024. p. 1-6. ISBN 979-8-3503-8754-4
dc.identifier.doi10.1109/aict61888.2024.10740453
dc.identifier.isbn979-8-3503-8754-4
dc.identifier.urihttp://hdl.handle.net/10198/34361
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relation.ispartof2024 IEEE 18th International Conference on Application of Information and Communication Technologies (AICT)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBusiness intelligence
dc.subjectHigher education
dc.subjectData engineering
dc.subjectDecision-making
dc.titleData Engineering Roadmap for Implementing Business Intelligence in Higher Educationeng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50014%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.endPage6
oaire.citation.startPage1
oaire.citation.title9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on Computer Science and Information Systems 2024, MCCSIS 2024
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAlves
person.givenNamePaulo
person.identifier.ciencia-idC319-FC42-5B6B
person.identifier.orcid0000-0002-0100-8691
person.identifier.scopus-author-id55834442100
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
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