Review of Data mining applications in forestry sector
- Autores
- Broz, Diego Ricardo; Olivera, Alejandro; Viana Céspedes, Víctor; Rossit, Daniel Alejandro
- Año de publicación
- 2017
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Modern technology makes possible to collect large amount of data that can be processed and transformed invaluable information for several human activities. Forest industry particularly can take advantage of suchtechnology because of modern forest harvesters are equipped with a system for data collection and communicationcalled StanForD. Data mining allows users to process large databases to determine trends and patterns. In thisextended abstract we present a brief revision of the literature dedicated to the issue and, also, we indicatesynthetically future research directions that could be useful for forest operations management. Some DMtechniques are artificial neural network and decision tree and they are used to perform association, classificationand clustering. Nonetheless, data mining techniques have been successfully applied to several fields, e.g. industry,marketing, sociology, economy, agriculture and environmental sciences.
Fil: Broz, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Misiones. Facultad de Ciencias Forestales; Argentina
Fil: Olivera, Alejandro. Universidad de la República; Uruguay
Fil: Viana Céspedes, Víctor. Universidad de la República; Uruguay
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
First International Conference on Agro Big Data and Decision Support Systems in Agriculture
Montevideo
Uruguay
Universidad de la República - Materia
-
BIG DATA
FOREST
REVIEW - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/138182
Ver los metadatos del registro completo
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Review of Data mining applications in forestry sectorBroz, Diego RicardoOlivera, AlejandroViana Céspedes, VíctorRossit, Daniel AlejandroBIG DATAFORESTREVIEWhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Modern technology makes possible to collect large amount of data that can be processed and transformed invaluable information for several human activities. Forest industry particularly can take advantage of suchtechnology because of modern forest harvesters are equipped with a system for data collection and communicationcalled StanForD. Data mining allows users to process large databases to determine trends and patterns. In thisextended abstract we present a brief revision of the literature dedicated to the issue and, also, we indicatesynthetically future research directions that could be useful for forest operations management. Some DMtechniques are artificial neural network and decision tree and they are used to perform association, classificationand clustering. Nonetheless, data mining techniques have been successfully applied to several fields, e.g. industry,marketing, sociology, economy, agriculture and environmental sciences.Fil: Broz, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Misiones. Facultad de Ciencias Forestales; ArgentinaFil: Olivera, Alejandro. Universidad de la República; UruguayFil: Viana Céspedes, Víctor. Universidad de la República; UruguayFil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFirst International Conference on Agro Big Data and Decision Support Systems in AgricultureMontevideoUruguayUniversidad de la RepúblicaUniversidad de la República2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectConferenciaBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/138182Review of Data mining applications in forestry sector; First International Conference on Agro Big Data and Decision Support Systems in Agriculture; Montevideo; Uruguay; 2017; 143-146CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=63228©ownerid=64337info:eu-repo/semantics/altIdentifier/url/http://www.bigdssagro.udl.cat/?q=node/75info:eu-repo/semantics/altIdentifier/url/http://www.bigdssagro.udl.cat/sites/default/files/Proceedings_bigDSSagro2017.pdfInternacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:38:15Zoai:ri.conicet.gov.ar:11336/138182instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:38:16.115CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Review of Data mining applications in forestry sector |
title |
Review of Data mining applications in forestry sector |
spellingShingle |
Review of Data mining applications in forestry sector Broz, Diego Ricardo BIG DATA FOREST REVIEW |
title_short |
Review of Data mining applications in forestry sector |
title_full |
Review of Data mining applications in forestry sector |
title_fullStr |
Review of Data mining applications in forestry sector |
title_full_unstemmed |
Review of Data mining applications in forestry sector |
title_sort |
Review of Data mining applications in forestry sector |
dc.creator.none.fl_str_mv |
Broz, Diego Ricardo Olivera, Alejandro Viana Céspedes, Víctor Rossit, Daniel Alejandro |
author |
Broz, Diego Ricardo |
author_facet |
Broz, Diego Ricardo Olivera, Alejandro Viana Céspedes, Víctor Rossit, Daniel Alejandro |
author_role |
author |
author2 |
Olivera, Alejandro Viana Céspedes, Víctor Rossit, Daniel Alejandro |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
BIG DATA FOREST REVIEW |
topic |
BIG DATA FOREST REVIEW |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.11 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
Modern technology makes possible to collect large amount of data that can be processed and transformed invaluable information for several human activities. Forest industry particularly can take advantage of suchtechnology because of modern forest harvesters are equipped with a system for data collection and communicationcalled StanForD. Data mining allows users to process large databases to determine trends and patterns. In thisextended abstract we present a brief revision of the literature dedicated to the issue and, also, we indicatesynthetically future research directions that could be useful for forest operations management. Some DMtechniques are artificial neural network and decision tree and they are used to perform association, classificationand clustering. Nonetheless, data mining techniques have been successfully applied to several fields, e.g. industry,marketing, sociology, economy, agriculture and environmental sciences. Fil: Broz, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Misiones. Facultad de Ciencias Forestales; Argentina Fil: Olivera, Alejandro. Universidad de la República; Uruguay Fil: Viana Céspedes, Víctor. Universidad de la República; Uruguay Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina First International Conference on Agro Big Data and Decision Support Systems in Agriculture Montevideo Uruguay Universidad de la República |
description |
Modern technology makes possible to collect large amount of data that can be processed and transformed invaluable information for several human activities. Forest industry particularly can take advantage of suchtechnology because of modern forest harvesters are equipped with a system for data collection and communicationcalled StanForD. Data mining allows users to process large databases to determine trends and patterns. In thisextended abstract we present a brief revision of the literature dedicated to the issue and, also, we indicatesynthetically future research directions that could be useful for forest operations management. Some DMtechniques are artificial neural network and decision tree and they are used to perform association, classificationand clustering. Nonetheless, data mining techniques have been successfully applied to several fields, e.g. industry,marketing, sociology, economy, agriculture and environmental sciences. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject Conferencia Book http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
status_str |
publishedVersion |
format |
conferenceObject |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/138182 Review of Data mining applications in forestry sector; First International Conference on Agro Big Data and Decision Support Systems in Agriculture; Montevideo; Uruguay; 2017; 143-146 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/138182 |
identifier_str_mv |
Review of Data mining applications in forestry sector; First International Conference on Agro Big Data and Decision Support Systems in Agriculture; Montevideo; Uruguay; 2017; 143-146 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=63228©ownerid=64337 info:eu-repo/semantics/altIdentifier/url/http://www.bigdssagro.udl.cat/?q=node/75 info:eu-repo/semantics/altIdentifier/url/http://www.bigdssagro.udl.cat/sites/default/files/Proceedings_bigDSSagro2017.pdf |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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Internacional |
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Universidad de la República |
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Universidad de la República |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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