Publication
A Proposed Architecture for IoT Big Data Analysis in Smart Supply Chain Fields
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | |
| datacite.subject.fos | Engenharia e Tecnologia::Outras Engenharias e Tecnologias | |
| dc.contributor.author | Constante-Nicolalde, Fabián-Vinicio | |
| dc.contributor.author | Pérez-Medina, Jorge-Luis | |
| dc.contributor.author | Guerra-Terán, Paulo | |
| dc.date.accessioned | 2025-10-22T16:23:32Z | |
| dc.date.available | 2025-10-22T16:23:32Z | |
| dc.date.issued | 2019-10-13 | |
| dc.description | Conference name - 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019; Conference date - 29 May 2019 - 31 May 2019; Conference code - 233339 | |
| dc.description | EISBN - 9783030320225 | |
| dc.description.abstract | The growth of large amounts of data in the last decade from Cloud Computing, Information Systems, and Digital Technologies with an increase in the production and miniaturization of Internet of Things (IoT) devices. However, these data without analytical power are not useful in any field. Concentration efforts at multiple levels are required for the extraction of knowledge and decision-making being the “Big Data Analysis” an area increasingly challenging. Numerous analysis solutions combining Big Data and IoT have allowed people to obtain valuable information. Big Data requires a certain complexity. Small Data is emerging as a more efficient alternative, since it combines structured and unstructured data that can be measured in Gigabytes, Peta bytes or Terabytes, forming part of small sets of specific IoT attributes. This article presents an architecture for the analysis of data generated by IoT. The proposed solution allows the extraction of knowledge, focusing on the case of specific use of the “Smart Supply Chain fields”. | eng |
| dc.description.sponsorship | This work was possible thanks “Universidad de Las Américas” from Ecuador for the financing of research and Leiria Polytechnic Institute from Portugal. | |
| dc.identifier.citation | Constante-Nicolalde, FV., Pérez-Medina, JL., Guerra-Terán, P. (2020). A Proposed Architecture for IoT Big Data Analysis in Smart Supply Chain Fields. In: Botto-Tobar, M., León-Acurio, J., Díaz Cadena, A., Montiel Díaz, P. (eds) Advances in Emerging Trends and Technologies. ICAETT 2019. Advances in Intelligent Systems and Computing, vol 1066. Springer, Cham. https://doi.org/10.1007/978-3-030-32022-5_34. | |
| dc.identifier.doi | 10.1007/978-3-030-32022-5_34 | |
| dc.identifier.eissn | 2194-5365 | |
| dc.identifier.isbn | 9783030320218 | |
| dc.identifier.isbn | 9783030320225 | |
| dc.identifier.issn | 2194-5357 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/14358 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Springer Nature | |
| dc.relation.hasversion | https://link.springer.com/chapter/10.1007/978-3-030-32022-5_34 | |
| dc.relation.ispartof | Advances in Intelligent Systems and Computing | |
| dc.relation.ispartof | Advances in Emerging Trends and Technologies | |
| dc.rights.uri | N/A | |
| dc.subject | Big Data Analysis | |
| dc.subject | Internet of Things | |
| dc.subject | Data mining | |
| dc.subject | Hadoop | |
| dc.subject | Radio frequency identification | |
| dc.title | A Proposed Architecture for IoT Big Data Analysis in Smart Supply Chain Fields | eng |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.conferenceDate | 2019-05 | |
| oaire.citation.conferencePlace | Quito, Ecuador | |
| oaire.citation.title | Advances in Intelligent Systems and Computing | |
| oaire.version | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 | |
| person.familyName | Constante | |
| person.givenName | Fabián | |
| person.identifier.orcid | 0000-0003-1747-6294 | |
| relation.isAuthorOfPublication | c96c2df4-2b4b-4b6a-a291-6242823cec30 | |
| relation.isAuthorOfPublication.latestForDiscovery | c96c2df4-2b4b-4b6a-a291-6242823cec30 |
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- The growth of large amounts of data in the last decade from Cloud Computing, Information Systems, and Digital Technologies with an increase in the production and miniaturization of Internet of Things (IoT) devices. However, these data without analytical power are not useful in any field. Concentration efforts at multiple levels are required for the extraction of knowledge and decision-making being the “Big Data Analysis” an area increasingly challenging. Numerous analysis solutions combining Big Data and IoT have allowed people to obtain valuable information. Big Data requires a certain complexity. Small Data is emerging as a more efficient alternative, since it combines structured and unstructured data that can be measured in Gigabytes, Peta bytes or Terabytes, forming part of small sets of specific IoT attributes. This article presents an architecture for the analysis of data generated by IoT. The proposed solution allows the extraction of knowledge, focusing on the case of specific use of the “Smart Supply Chain fields”.
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