Decision Tree Algorithm Moderately Coupled to PostgreSQL DBMS
Using machine learning for data management is an extraordinary opportunity to move towards a leadership model based on information, which drives the organization towards success in each initiative. However, when incorporating these technologies, a company presents problems associated with the econom...
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| Format: | Online |
| Language: | eng |
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Universidad Pedagógica y Tecnológica de Colombia
2023
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16777 |
| _version_ | 1801706104957173760 |
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| author | Timarán-Pereira, Ricardo Chaves-Torres, Anivar Ordoñez-Erazo, Hugo |
| author_facet | Timarán-Pereira, Ricardo Chaves-Torres, Anivar Ordoñez-Erazo, Hugo |
| author_sort | Timarán-Pereira, Ricardo |
| collection | OJS |
| description | Using machine learning for data management is an extraordinary opportunity to move towards a leadership model based on information, which drives the organization towards success in each initiative. However, when incorporating these technologies, a company presents problems associated with the economic and administrative costs generated in this process since these are usually quite high, limiting their implementation in MSMEs. This paper proposes to integrate supervised machine learning techniques into PostgreSQL DBMS in a moderately coupled architecture to provide it with the capabilities of discovering knowledge in databases. Classification and regression algorithms were coupled by developing extensions using one of the procedural languages supported by PostgreSQL. Initially, the C4.5 decision tree classification algorithm was implemented using the PL/pgSQL procedural language. The main advantage of this strategy is that it considers the scalability, administration, and data manipulation of the DBMS. Since PostgreSQL is an open-source manager, organizations such as MSMEs will have a free tool that allows them to perform predictive analysis in order to improve their decision-making processes by anticipating future consumer behavior and making rational decisions based on their findings. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-16777 |
| institution | Revista Facultad de Ingeniería |
| language | eng |
| publishDate | 2023 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-167772024-02-23T14:02:39Z Decision Tree Algorithm Moderately Coupled to PostgreSQL DBMS Algoritmo de árboles de decisión medianamente acoplado a PostgreSQL Timarán-Pereira, Ricardo Chaves-Torres, Anivar Ordoñez-Erazo, Hugo classification techniques C4.5 algorithm middle coupled architecture PostgreSQL DBMS técnicas de clasificación algoritmo C4.5 arquitectura medianamente acoplada PostgreSQL Using machine learning for data management is an extraordinary opportunity to move towards a leadership model based on information, which drives the organization towards success in each initiative. However, when incorporating these technologies, a company presents problems associated with the economic and administrative costs generated in this process since these are usually quite high, limiting their implementation in MSMEs. This paper proposes to integrate supervised machine learning techniques into PostgreSQL DBMS in a moderately coupled architecture to provide it with the capabilities of discovering knowledge in databases. Classification and regression algorithms were coupled by developing extensions using one of the procedural languages supported by PostgreSQL. Initially, the C4.5 decision tree classification algorithm was implemented using the PL/pgSQL procedural language. The main advantage of this strategy is that it considers the scalability, administration, and data manipulation of the DBMS. Since PostgreSQL is an open-source manager, organizations such as MSMEs will have a free tool that allows them to perform predictive analysis in order to improve their decision-making processes by anticipating future consumer behavior and making rational decisions based on their findings. El uso de Aprendizaje Automático para la gestión de datos es una oportunidad extraordinaria para avanzar hacia un modelo de liderazgo basado en la información, que impulse a la organización hacia el éxito en cada una de sus iniciativas. Sin embargo, una empresa, en el momento de incorporar estas tecnologías presenta problemáticas asociadas con los costos económicos y administrativos generados en este proceso, ya que estos suelen ser bastante elevados, que limita principalmente a las MiPymes, su implementación. En este artículo se presenta la propuesta de integrar al SGBD PostgreSQL, técnicas supervisadas de aprendizaje automático, en una arquitectura medianamente acoplada, con el fin de dotar a este gestor con las capacidades de descubrir conocimiento en las bases de datos. Se acoplarán algoritmos de clasificación y regresión mediante el desarrollo de extensiones utilizando uno de los lenguajes procedurales soportados por PostgreSQL. Inicialmente, se implementará el algoritmo de clasificación por árboles de decisión C4.5 usando el lenguaje procedural PL/pgSQL. La principal ventaja de esta estrategia es que se tiene en cuenta la escalabilidad, administración y manipulación de datos del SGBD. Al ser PostgreSQL un gestor de código abierto, organizaciones tales como MiPymes, contarán con una herramienta libre que les permita realizar análisis predictivo con el fin mejorar sus procesos de toma de decisiones al poder anticiparse a los futuros comportamientos del consumidor y tomar decisiones racionales basadas en sus hallazgos. Universidad Pedagógica y Tecnológica de Colombia 2023-11-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16777 Revista Facultad de Ingeniería; Vol. 32 No. 66 (2023): October-December 2023 (Continuous Publication); e16777 Revista Facultad de Ingeniería; Vol. 32 Núm. 66 (2023): Octubre-Diciembre 2023 (Publicación Continua) ; e16777 2357-5328 0121-1129 eng https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16777/13615 Copyright (c) 2023 Ricardo Timarán-Pereira, Anivar Chaves-Torres, Hugo Ordoñez-Erazo http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | classification techniques C4.5 algorithm middle coupled architecture PostgreSQL DBMS técnicas de clasificación algoritmo C4.5 arquitectura medianamente acoplada PostgreSQL Timarán-Pereira, Ricardo Chaves-Torres, Anivar Ordoñez-Erazo, Hugo Decision Tree Algorithm Moderately Coupled to PostgreSQL DBMS |
| title | Decision Tree Algorithm Moderately Coupled to PostgreSQL DBMS |
| title_alt | Algoritmo de árboles de decisión medianamente acoplado a PostgreSQL |
| title_full | Decision Tree Algorithm Moderately Coupled to PostgreSQL DBMS |
| title_fullStr | Decision Tree Algorithm Moderately Coupled to PostgreSQL DBMS |
| title_full_unstemmed | Decision Tree Algorithm Moderately Coupled to PostgreSQL DBMS |
| title_short | Decision Tree Algorithm Moderately Coupled to PostgreSQL DBMS |
| title_sort | decision tree algorithm moderately coupled to postgresql dbms |
| topic | classification techniques C4.5 algorithm middle coupled architecture PostgreSQL DBMS técnicas de clasificación algoritmo C4.5 arquitectura medianamente acoplada PostgreSQL |
| topic_facet | classification techniques C4.5 algorithm middle coupled architecture PostgreSQL DBMS técnicas de clasificación algoritmo C4.5 arquitectura medianamente acoplada PostgreSQL |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16777 |
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