Integration of data mining techniques to postgreSQL database manager system

Data mining is a technique that allows to obtain patterns or models from the gathered data. This technique is applied in all kind of environments such as in the biological field, educational and financial applications, industry, police, and political processes. Within data mining there are several t...

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Autores:
amelec, viloria
Camargo ACUÑA, Genesis Yulie
Alcázar Franco, Daniel Jesús
Hernández-Palma, Hugo
Fuentes-Pacheco, Jorge
Pallares Rambal, Etelberto
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5860
Acceso en línea:
http://hdl.handle.net/11323/5860
https://doi.org/10.1016/j.procs.2019.08.080
https://repositorio.cuc.edu.co/
Palabra clave:
Data mining
Database management system
PostgreSQL
Decision-making trees
Induction rules
Procesamiento de datos
Sistema de administración de base de datos
PostgreSQL
Árboles de toma de decisiones
Reglas de inducción
Rights
openAccess
License
http://creativecommons.org/publicdomain/zero/1.0/
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/5860
network_acronym_str RCUC2
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repository_id_str
dc.title.spa.fl_str_mv Integration of data mining techniques to postgreSQL database manager system
dc.title.translated.spa.fl_str_mv Integración de técnicas de minería de datos al sistema de administrador de base de datos postgreSQL
title Integration of data mining techniques to postgreSQL database manager system
spellingShingle Integration of data mining techniques to postgreSQL database manager system
Data mining
Database management system
PostgreSQL
Decision-making trees
Induction rules
Procesamiento de datos
Sistema de administración de base de datos
PostgreSQL
Árboles de toma de decisiones
Reglas de inducción
title_short Integration of data mining techniques to postgreSQL database manager system
title_full Integration of data mining techniques to postgreSQL database manager system
title_fullStr Integration of data mining techniques to postgreSQL database manager system
title_full_unstemmed Integration of data mining techniques to postgreSQL database manager system
title_sort Integration of data mining techniques to postgreSQL database manager system
dc.creator.fl_str_mv amelec, viloria
Camargo ACUÑA, Genesis Yulie
Alcázar Franco, Daniel Jesús
Hernández-Palma, Hugo
Fuentes-Pacheco, Jorge
Pallares Rambal, Etelberto
dc.contributor.author.spa.fl_str_mv amelec, viloria
Camargo ACUÑA, Genesis Yulie
Alcázar Franco, Daniel Jesús
Hernández-Palma, Hugo
Fuentes-Pacheco, Jorge
Pallares Rambal, Etelberto
dc.subject.spa.fl_str_mv Data mining
Database management system
PostgreSQL
Decision-making trees
Induction rules
Procesamiento de datos
Sistema de administración de base de datos
PostgreSQL
Árboles de toma de decisiones
Reglas de inducción
topic Data mining
Database management system
PostgreSQL
Decision-making trees
Induction rules
Procesamiento de datos
Sistema de administración de base de datos
PostgreSQL
Árboles de toma de decisiones
Reglas de inducción
description Data mining is a technique that allows to obtain patterns or models from the gathered data. This technique is applied in all kind of environments such as in the biological field, educational and financial applications, industry, police, and political processes. Within data mining there are several techniques, among which are the induction of rules and decision trees which, according to various studies carried out, are among the most used. This research analyzes decision tree data mining techniques and induction rules to integrate several of its algorithms into PostgreSQL database management system (DBMS). Through an experiment, it was found that when the algorithms are integrated to the manager, the response times and the results obtained are higher.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-01-17T19:31:30Z
dc.date.available.none.fl_str_mv 2020-01-17T19:31:30Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 1877-0509
dc.identifier.uri.spa.fl_str_mv http://hdl.handle.net/11323/5860
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.procs.2019.08.080
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1877-0509
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url http://hdl.handle.net/11323/5860
https://doi.org/10.1016/j.procs.2019.08.080
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.
Viloria, A., Viviana Robayo, P.: Virtual network level of application composed IP networks connected with systems - (NETS Peer-to- Peer). Indian J. Sci. Technol. (2016). ISSN 0974-5645.
Balaguera MI., Vargas MC., Lis-Gutierrez JP., Viloria A., Malagón L.E. (2018) Architecture of an Object-Oriented Modeling Framework for Human Occupation. In: Tan Y., Shi Y., Tang Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham.
Fairley, R.E., “Recent advances in software estimation techniques”, Proceedings of the 14th international conference on Software engineering, Melbourne, Australia, 1992, pp.382 – 391.
Walkerden, F. y Jeffery, D., “Software cost estimation: A review of models, process, and practice”, Advances in Computers, Vol. 44, 1997, pp. 59-125.
Boehm, B., Abts, C. y Chulani, S., “Software development cost estimation approaches-a survey”, Annals of Software Engineering 10, 2000, pp. 177-205
Wieczorek, I. y Briand, L., Resource estimation in software engineering, Technical Report, International Software Engineering Research Network, 2001.
Piotrowski, A.P., 2017. Review of Differential Evolution population size. Swarm Evol. Comput. 32, 1–24. https://doi.org/10.1016/j.swevo.2016.05.003
Kaya, I., 2009. A genetic algorithm approach to determine the sample size for attribute control charts. Inf. Sci. (Ny). 179, 1552– 1566. https://doi.org/10.1016/j.ins.2008.09.024
Gaitán-Angulo M, Jairo Enrique Santander Abril, Amelec Viloria, Julio Mojica Herazo, Pedro Hernández Malpica, Jairo Luis Martínez Ventura, Lissette Hernández-Fernández. (2018) Company Family, Innovation and Colombian Graphic Industry: A Bayesian Estimation of a Logistical Model. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.
Amelec, Viloria. "Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy." Advanced Science Letters 21.5 (2015): 1406-1408.
MAcQueen, J., 1967. Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkeley Symp. Math. Stat. Probab 1, 281–297.
Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M., 2018. Inice: A New Approach for Identifying
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spelling amelec, viloriaCamargo ACUÑA, Genesis YulieAlcázar Franco, Daniel JesúsHernández-Palma, HugoFuentes-Pacheco, JorgePallares Rambal, Etelberto2020-01-17T19:31:30Z2020-01-17T19:31:30Z20191877-0509http://hdl.handle.net/11323/5860https://doi.org/10.1016/j.procs.2019.08.080Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Data mining is a technique that allows to obtain patterns or models from the gathered data. This technique is applied in all kind of environments such as in the biological field, educational and financial applications, industry, police, and political processes. Within data mining there are several techniques, among which are the induction of rules and decision trees which, according to various studies carried out, are among the most used. This research analyzes decision tree data mining techniques and induction rules to integrate several of its algorithms into PostgreSQL database management system (DBMS). Through an experiment, it was found that when the algorithms are integrated to the manager, the response times and the results obtained are higher.La minería de datos es una técnica que permite obtener patrones o modelos a partir de los datos recopilados. Esta técnica se aplica en todo tipo de entornos, como el campo biológico, las aplicaciones educativas y financieras, la industria, la policía y los procesos políticos. Dentro de la minería de datos existen varias técnicas, entre las cuales se encuentran la inducción de reglas y árboles de decisión que, según diversos estudios realizados, se encuentran entre las más utilizadas. Esta investigación analiza las técnicas de extracción de datos del árbol de decisiones y las reglas de inducción para integrar varios de sus algoritmos en el sistema de gestión de bases de datos PostgreSQL (DBMS). A través de un experimento, se descubrió que cuando los algoritmos se integran al administrador, los tiempos de respuesta y los resultados obtenidos son más altos.Amelec, Viloria-will be generated-orcid-0000-0003-2673-6350-600Camargo Acuña, Genesis Yulie-will be generated-orcid-0000-0003-0425-3083-600Alcázar Franco, Daniel JesúsHernández-Palma, HugoFuentes Pacheco, Jorge-will be generated-orcid-0000-0002-9060-8276-600Pallares Rambal, EtelbertoengProcedia Computer Sciencehttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Data miningDatabase management systemPostgreSQLDecision-making treesInduction rulesProcesamiento de datosSistema de administración de base de datosPostgreSQLÁrboles de toma de decisionesReglas de inducciónIntegration of data mining techniques to postgreSQL database manager systemIntegración de técnicas de minería de datos al sistema de administrador de base de datos postgreSQLArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionViloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.Viloria, A., Viviana Robayo, P.: Virtual network level of application composed IP networks connected with systems - (NETS Peer-to- Peer). Indian J. Sci. Technol. (2016). ISSN 0974-5645.Balaguera MI., Vargas MC., Lis-Gutierrez JP., Viloria A., Malagón L.E. (2018) Architecture of an Object-Oriented Modeling Framework for Human Occupation. In: Tan Y., Shi Y., Tang Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham.Fairley, R.E., “Recent advances in software estimation techniques”, Proceedings of the 14th international conference on Software engineering, Melbourne, Australia, 1992, pp.382 – 391.Walkerden, F. y Jeffery, D., “Software cost estimation: A review of models, process, and practice”, Advances in Computers, Vol. 44, 1997, pp. 59-125.Boehm, B., Abts, C. y Chulani, S., “Software development cost estimation approaches-a survey”, Annals of Software Engineering 10, 2000, pp. 177-205Wieczorek, I. y Briand, L., Resource estimation in software engineering, Technical Report, International Software Engineering Research Network, 2001.Piotrowski, A.P., 2017. Review of Differential Evolution population size. Swarm Evol. Comput. 32, 1–24. https://doi.org/10.1016/j.swevo.2016.05.003Kaya, I., 2009. A genetic algorithm approach to determine the sample size for attribute control charts. Inf. Sci. (Ny). 179, 1552– 1566. https://doi.org/10.1016/j.ins.2008.09.024Gaitán-Angulo M, Jairo Enrique Santander Abril, Amelec Viloria, Julio Mojica Herazo, Pedro Hernández Malpica, Jairo Luis Martínez Ventura, Lissette Hernández-Fernández. (2018) Company Family, Innovation and Colombian Graphic Industry: A Bayesian Estimation of a Logistical Model. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.Amelec, Viloria. "Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy." Advanced Science Letters 21.5 (2015): 1406-1408.MAcQueen, J., 1967. Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkeley Symp. Math. Stat. Probab 1, 281–297.Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M., 2018. Inice: A New Approach for IdentifyingPublicationORIGINALIntegration of Data Mining.pdfIntegration of Data Mining.pdfapplication/pdf420864https://repositorio.cuc.edu.co/bitstreams/cbf8bfb9-1b95-4cb0-8952-70a3d6a2244d/download68a887f484973cfccc7e821e8fa251f2MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/9cf4a806-2d7a-4c89-b241-82bb83730f44/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/7bc8298b-9832-4e91-9daa-17537a10e7e5/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILIntegration of Data Mining.pdf.jpgIntegration of Data Mining.pdf.jpgimage/jpeg42493https://repositorio.cuc.edu.co/bitstreams/16478bc2-d460-424d-8c80-0f461eda9408/download9500892ab73d5f05e0b5b9a1650b973dMD55TEXTIntegration of Data Mining.pdf.txtIntegration of Data Mining.pdf.txttext/plain18773https://repositorio.cuc.edu.co/bitstreams/5b04752e-54b0-4664-98bf-7fb696b08a5b/download6dd9dd37995d797fca853d60fc7f719dMD5611323/5860oai:repositorio.cuc.edu.co:11323/58602024-09-16 16:36:24.399http://creativecommons.org/publicdomain/zero/1.0/open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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