Data Mining Streams of Social Networks, A Tool to Improve The Library Services

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The Groupware systems are a valuable source for disseminating information in contexts in which the participation of a group of people is required to perform a task. One such context is the Library, Archives and Documentation. The interactions among users and professionals in this area, who use tools...

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Autores principales: Jaramillo Valbuena, Sonia, Cardona, Sergio Augusto, Fernandez, Alejandro
Formato: Artículo publishedVersion
Lenguaje:Español
Publicado: Universidad de Buenos Aires, Facultad de Filosofía y Letras 2015
Materias:
Data stream mining
Classification
Clustering
Concept-drift
CSCW
Minería de flujos de datos
Clasificación
Cambio de concepto
Sistemas de soporte al trabajo colaborativo
Acceso en línea:https://revistascientificas.filo.uba.ar/index.php/ICS/article/view/1182
https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=biblioinfo&d=1182_oai
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Repositorio Digital de la Universidad de Buenos Aires (UBA) de Universidad de Buenos Aires
id I28-R145-1182_oai
record_format dspace
spelling I28-R145-1182_oai2025-11-17 Jaramillo Valbuena, Sonia Cardona, Sergio Augusto Fernandez, Alejandro 2015-11-17 The Groupware systems are a valuable source for disseminating information in contexts in which the participation of a group of people is required to perform a task. One such context is the Library, Archives and Documentation. The interactions among users and professionals in this area, who use tools such as Twitter, Facebook, RSS feeds and blogs, generate a large amount of unstructured data streams. They can be used to the problem of mining topic-specific influence, graph mining, opinion mining and recommender systems, thus achieving that libraries can obtain maximum benefit from the use of Information and Communication Technologies. From the perspective of data stream mining, the processing of these streams poses significant challenges. The algorithms must be adapted to problems such as: high arrival rate, memory requirements without restrictions, diverse sources of data and concept-drift. In this work, we explore the current state-of-the-art solutions of data stream mining originating from social networks, specifically, Facebook and Twitter. We present a review of the most representative algorithms and how they contribute to knowledge discovery in the area of librarianship. We conclude by presenting some of the problems that are the subject of active research. Los sistemas de soporte al trabajo colaborativo, Groupware, son una herramienta valiosa en contextos en los cuales se requiere la participación de un grupo de personas para llevar a cabo una tarea. Las interacciones entre las personas que los utilizan generan grandes flujos de datos (streams) no estructurados. Estos streams pueden analizarse para estudiar aspectos tales como influencia, relaciones de cercanía, opinión y para la generación de recomendaciones. Desde la perspectiva de la minería de datos, el procesamiento de estos streams plantea importantes desafíos. Los algoritmos de minería a utilizar deben adaptarse a la alta velocidad en que llegan los datos, a la diversidad de las fuentes de datos y su estructura, a variabilidad de los datos en el tiempo y a trabajar sin restricciones de memoria.Este artículo revisa el estado del arte en lo referente a algoritmos de minería de datos sobre streams originados en sistemas groupware. Se presenta una revisión de las técnicas más representativas y de cómo cada una de ellas aporta al descubrimiento de conocimiento. Específicamente, se analiza la gestión de la información proveniente de redes sociales.  Para concluir se presentan algunos de los problemas que son objeto de investigación activa. application/pdf application/msword https://revistascientificas.filo.uba.ar/index.php/ICS/article/view/1182 10.34096/ics.i33.1182 spa Universidad de Buenos Aires, Facultad de Filosofía y Letras https://revistascientificas.filo.uba.ar/index.php/ICS/article/view/1182/1794 https://revistascientificas.filo.uba.ar/index.php/ICS/article/view/1182/6190 Información, cultura y sociedad; No. 33 (2015): Diciembre; 63-74 Información, cultura y sociedad; Núm. 33 (2015): Diciembre; 63-74 1851-1740 1514-8327 Data stream mining Classification Clustering Concept-drift CSCW Minería de flujos de datos Clasificación Clustering Cambio de concepto Sistemas de soporte al trabajo colaborativo Data Mining Streams of Social Networks, A Tool to Improve The Library Services Minería de datos sobre streams de redes sociales, una herramienta al servicio de la Bibliotecología info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=biblioinfo&d=1182_oai
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-145
collection Repositorio Digital de la Universidad de Buenos Aires (UBA)
language Español
orig_language_str_mv spa
topic Data stream mining
Classification
Clustering
Concept-drift
CSCW
Minería de flujos de datos
Clasificación
Clustering
Cambio de concepto
Sistemas de soporte al trabajo colaborativo
spellingShingle Data stream mining
Classification
Clustering
Concept-drift
CSCW
Minería de flujos de datos
Clasificación
Clustering
Cambio de concepto
Sistemas de soporte al trabajo colaborativo
Jaramillo Valbuena, Sonia
Cardona, Sergio Augusto
Fernandez, Alejandro
Data Mining Streams of Social Networks, A Tool to Improve The Library Services
topic_facet Data stream mining
Classification
Clustering
Concept-drift
CSCW
Minería de flujos de datos
Clasificación
Clustering
Cambio de concepto
Sistemas de soporte al trabajo colaborativo
description The Groupware systems are a valuable source for disseminating information in contexts in which the participation of a group of people is required to perform a task. One such context is the Library, Archives and Documentation. The interactions among users and professionals in this area, who use tools such as Twitter, Facebook, RSS feeds and blogs, generate a large amount of unstructured data streams. They can be used to the problem of mining topic-specific influence, graph mining, opinion mining and recommender systems, thus achieving that libraries can obtain maximum benefit from the use of Information and Communication Technologies. From the perspective of data stream mining, the processing of these streams poses significant challenges. The algorithms must be adapted to problems such as: high arrival rate, memory requirements without restrictions, diverse sources of data and concept-drift. In this work, we explore the current state-of-the-art solutions of data stream mining originating from social networks, specifically, Facebook and Twitter. We present a review of the most representative algorithms and how they contribute to knowledge discovery in the area of librarianship. We conclude by presenting some of the problems that are the subject of active research.
format Artículo
publishedVersion
author Jaramillo Valbuena, Sonia
Cardona, Sergio Augusto
Fernandez, Alejandro
author_facet Jaramillo Valbuena, Sonia
Cardona, Sergio Augusto
Fernandez, Alejandro
author_sort Jaramillo Valbuena, Sonia
title Data Mining Streams of Social Networks, A Tool to Improve The Library Services
title_short Data Mining Streams of Social Networks, A Tool to Improve The Library Services
title_full Data Mining Streams of Social Networks, A Tool to Improve The Library Services
title_fullStr Data Mining Streams of Social Networks, A Tool to Improve The Library Services
title_full_unstemmed Data Mining Streams of Social Networks, A Tool to Improve The Library Services
title_sort data mining streams of social networks, a tool to improve the library services
publisher Universidad de Buenos Aires, Facultad de Filosofía y Letras
publishDate 2015
url https://revistascientificas.filo.uba.ar/index.php/ICS/article/view/1182
https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=biblioinfo&d=1182_oai
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