Linked data to streaming data sensors sources for direct access
Autor(a) principal: | |
---|---|
Data de Publicação: | 2024 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Operations & Production Management (Online) |
Texto Completo: | https://bjopm.org.br/bjopm/article/view/1993 |
Resumo: | Goal: Industrial operations are complex, and data sensors assure safety and reliable information for production improvements. Multiple stakeholders can take advantage of data acquisition for post-analysis and process control. Providing users and systems with friendly access to operating data is fundamental to the digital transition in the industry 4.0 scenario. Linking data and systems over ontologies and Industry Foundation Classes will boost supply chain performance in many layers. This paper presents the concept of valid data points over Uniform Resource Identifiers for sensor time-series into triples stores via Application Programming Interfaces. Design/Methodology/Approach: A streaming data source approach to integrating industrial sensor data and sharing it via Uniform Resource Identifiers is developed and tested using Node-Red with multiple data connection types, such as the Industry Foundation Classes and open-source time series databases. Results: The detailed proof of concept presented valid the feasibility of sharing sensor data via Uniform Resource Identifiers. The findings provide a backbone of a system able to interop Message Queuing Telemetry Transport data, Resource Description Framework datasets and Industry Foundation Classes schema. Limitations of the investigation: The system envisaged was tested using simulated data. However, it is expected to have similar results from real data use. Nevertheless, more research will be needed to implement more features, such as three-dimensional object integration. Practical implications: The solution designed and tested presented can be used in practice for companies that desire to expand via linked data shareability and interoperability. Also, researchers can advance the solution for specific features, such as creating an open-source data query and manipulation language. Originality/Value: This paper examines future deployments of systems-to-systems interoperability targeting user-friendly data shareability. It is meant to be useful for industrial and academic developments. |
id |
ABEPRO_aeabc103f69ae688cc1b231230102d1f |
---|---|
oai_identifier_str |
oai:ojs.bjopm.org.br:article/1993 |
network_acronym_str |
ABEPRO |
network_name_str |
Brazilian Journal of Operations & Production Management (Online) |
repository_id_str |
|
spelling |
Linked data to streaming data sensors sources for direct accessIndustry 4.0Linked systemsServer-Sent EventsTime-seriesMQTTGoal: Industrial operations are complex, and data sensors assure safety and reliable information for production improvements. Multiple stakeholders can take advantage of data acquisition for post-analysis and process control. Providing users and systems with friendly access to operating data is fundamental to the digital transition in the industry 4.0 scenario. Linking data and systems over ontologies and Industry Foundation Classes will boost supply chain performance in many layers. This paper presents the concept of valid data points over Uniform Resource Identifiers for sensor time-series into triples stores via Application Programming Interfaces. Design/Methodology/Approach: A streaming data source approach to integrating industrial sensor data and sharing it via Uniform Resource Identifiers is developed and tested using Node-Red with multiple data connection types, such as the Industry Foundation Classes and open-source time series databases. Results: The detailed proof of concept presented valid the feasibility of sharing sensor data via Uniform Resource Identifiers. The findings provide a backbone of a system able to interop Message Queuing Telemetry Transport data, Resource Description Framework datasets and Industry Foundation Classes schema. Limitations of the investigation: The system envisaged was tested using simulated data. However, it is expected to have similar results from real data use. Nevertheless, more research will be needed to implement more features, such as three-dimensional object integration. Practical implications: The solution designed and tested presented can be used in practice for companies that desire to expand via linked data shareability and interoperability. Also, researchers can advance the solution for specific features, such as creating an open-source data query and manipulation language. Originality/Value: This paper examines future deployments of systems-to-systems interoperability targeting user-friendly data shareability. It is meant to be useful for industrial and academic developments.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2024-01-24info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionResearch paperapplication/pdfhttps://bjopm.org.br/bjopm/article/view/199310.14488/BJOPM.1993.2024Brazilian Journal of Operations & Production Management; Vol. 21 No. 1 (2024)2237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/1993/1065Copyright (c) 2024 Diego Calvetti, Daniel Luiz de Mattos Nascimento, Flávio Ney Magno de Araújo Filho, Rafael Henrique Viana Abreu, Nicolas Alexandros Papadopoulos http://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCalvetti, DiegoNascimento, Daniel Luiz de MattosAraújo Filho, Flávio Ney Magno deAbreu, Rafael Henrique VianaPapadopoulos , Nicolas Alexandros2024-01-24T12:11:17Zoai:ojs.bjopm.org.br:article/1993Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2024-01-24T12:11:17Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Linked data to streaming data sensors sources for direct access |
title |
Linked data to streaming data sensors sources for direct access |
spellingShingle |
Linked data to streaming data sensors sources for direct access Calvetti, Diego Industry 4.0 Linked systems Server-Sent Events Time-series MQTT |
title_short |
Linked data to streaming data sensors sources for direct access |
title_full |
Linked data to streaming data sensors sources for direct access |
title_fullStr |
Linked data to streaming data sensors sources for direct access |
title_full_unstemmed |
Linked data to streaming data sensors sources for direct access |
title_sort |
Linked data to streaming data sensors sources for direct access |
author |
Calvetti, Diego |
author_facet |
Calvetti, Diego Nascimento, Daniel Luiz de Mattos Araújo Filho, Flávio Ney Magno de Abreu, Rafael Henrique Viana Papadopoulos , Nicolas Alexandros |
author_role |
author |
author2 |
Nascimento, Daniel Luiz de Mattos Araújo Filho, Flávio Ney Magno de Abreu, Rafael Henrique Viana Papadopoulos , Nicolas Alexandros |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Calvetti, Diego Nascimento, Daniel Luiz de Mattos Araújo Filho, Flávio Ney Magno de Abreu, Rafael Henrique Viana Papadopoulos , Nicolas Alexandros |
dc.subject.por.fl_str_mv |
Industry 4.0 Linked systems Server-Sent Events Time-series MQTT |
topic |
Industry 4.0 Linked systems Server-Sent Events Time-series MQTT |
description |
Goal: Industrial operations are complex, and data sensors assure safety and reliable information for production improvements. Multiple stakeholders can take advantage of data acquisition for post-analysis and process control. Providing users and systems with friendly access to operating data is fundamental to the digital transition in the industry 4.0 scenario. Linking data and systems over ontologies and Industry Foundation Classes will boost supply chain performance in many layers. This paper presents the concept of valid data points over Uniform Resource Identifiers for sensor time-series into triples stores via Application Programming Interfaces. Design/Methodology/Approach: A streaming data source approach to integrating industrial sensor data and sharing it via Uniform Resource Identifiers is developed and tested using Node-Red with multiple data connection types, such as the Industry Foundation Classes and open-source time series databases. Results: The detailed proof of concept presented valid the feasibility of sharing sensor data via Uniform Resource Identifiers. The findings provide a backbone of a system able to interop Message Queuing Telemetry Transport data, Resource Description Framework datasets and Industry Foundation Classes schema. Limitations of the investigation: The system envisaged was tested using simulated data. However, it is expected to have similar results from real data use. Nevertheless, more research will be needed to implement more features, such as three-dimensional object integration. Practical implications: The solution designed and tested presented can be used in practice for companies that desire to expand via linked data shareability and interoperability. Also, researchers can advance the solution for specific features, such as creating an open-source data query and manipulation language. Originality/Value: This paper examines future deployments of systems-to-systems interoperability targeting user-friendly data shareability. It is meant to be useful for industrial and academic developments. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-01-24 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Research paper |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/1993 10.14488/BJOPM.1993.2024 |
url |
https://bjopm.org.br/bjopm/article/view/1993 |
identifier_str_mv |
10.14488/BJOPM.1993.2024 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/1993/1065 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
dc.source.none.fl_str_mv |
Brazilian Journal of Operations & Production Management; Vol. 21 No. 1 (2024) 2237-8960 reponame:Brazilian Journal of Operations & Production Management (Online) instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Brazilian Journal of Operations & Production Management (Online) |
collection |
Brazilian Journal of Operations & Production Management (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
bjopm.journal@gmail.com |
_version_ |
1797051459497361408 |