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dc.date.accessioned 2023-07-11T18:05:58Z
dc.date.available 2023-07-11T18:05:58Z
dc.date.issued 2023
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/155442
dc.description.abstract As the deployment of ocean sensors continues to grow, there is a growing need for standardized ways to represent and integrate sensor data from different sources. One approach to achieving this is through the use of Semantic Sensor Network (SSN/SOSA) ontology, which provides a common vocabulary and framework for describing sensors, observations, and their properties. In this paper, we present a method for converting ocean sensor data in CSV format to Resource Description Framework (RDF) using RDFLib library for Phyton and SSN/SOSA ontology. The resulting RDF triples can be stored in a triplestore for querying and analysis, providing a standardized representation of ocean sensor data that can be easily integrated with other data sources. en
dc.format.extent 53-57 es
dc.language en es
dc.subject Ocean sensors es
dc.subject SSN/SOSA ontology es
dc.subject RDFLib es
dc.subject Aqualink es
dc.title Integrating oceanographic sensor data using SSN/SOSA ontology en
dc.type Objeto de conferencia es
sedici.identifier.isbn 978-950-34-2271-7 es
sedici.creator.person Nuñez, Gustavo es
sedici.creator.person Buckle, Carlos Ezequiel es
sedici.creator.person Zárate, Marcos Daniel es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.date.exposure 2023-06
sedici.relation.event XI Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 27 al 29 de junio de 2023) es
sedici.description.peerReview peer-review es
sedici.relation.isRelatedWith http://sedici.unlp.edu.ar/handle/10915/155281 es
sedici.relation.bookTitle XI Jornadas de Cloud Computing, Big Data & Emerging Topics es


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)