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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/138182
Acceder
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spelling 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&copyownerid=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 Técnicas2025-10-22T12:10:23Zoai: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:34982025-10-22 12:10:24.174CONICET 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&copyownerid=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
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv Universidad de la República
publisher.none.fl_str_mv Universidad de la República
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
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repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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