Text Analytics: the convergence of Big Data and Artificial Intelligence

Moreno, Antonio; Redondo, Teófilo

doi:10.9781/ijimai.2016.369

Cited by 97 publications

(45 citation statements)

References 17 publications

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“…A problem that has been widely studied is how to find the characteristic words of a document (e.g., [44][45][46][47]). ese characteristic words can be used, for instance, to implement keywordbased document search.…”

Section: Characteristic Words Of a Documentmentioning

confidence: 99%

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

Hernández

Pérez

Granados

et al. 2019

Scientific Programming

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This paper addresses the problem of automatically customizing the sending of notifications in a nondisturbing way, that is, by using only implicit-feedback. Then, we build a hybrid filter that combines text mining content filtering and collaborative filtering to predict the notifications that are most interesting for each user. The content-based filter clusters notifications to find content with topics for which the user has shown interest. The collaborative filter increases diversity by discovering new topics of interest for the user, because these are of interest to other users with similar concerns. The paper reports the result of measuring the performance of this recommender and includes a validation of the topics-based approach used for content selection. Finally, we demonstrate how the recommender uses implicit-feedback to personalize the content to be delivered to each user.

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Section: Characteristic Words Of a Documentmentioning

confidence: 99%

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

Hernández

Pérez

Granados

et al. 2019

Scientific Programming

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“…Text mining techniques can be used to extract knowledge from unstructured or semi-structured textual data, and they have widespread applications in analyzing and processing textual documents. Such text analytics enable the discovery of previously unknown information by automatically extracting information from various written resources (Moreno & Redondo, 2016). Further, combining textual mining techniques with bibliometric analysis helps us discover more unseen patterns in research fields than with simple bibliometric analysis alone (Nie & Sun, 2017).…”

Section: A Topic Modelling Analysis Of Living Labs Researchmentioning

confidence: 99%

A Topic Modelling Analysis of Living Labs Research

Westerlund¹,

Leminen²,

Rajahonka³

2018

TIM Review

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“…Este crecimiento ha originado el término Big Data [4]. Aunque existen varias definiciones, Big Data se refiere a enormes volúmenes de datos (estructurados, semiestructurados y no-estructurados) del orden de exabytes (10 18 bytes) cuyo almacenamiento y análisis (e.g., análisis textual [5] de mensajes de correo, tweets, blogs) se puede hacer mediante BD especializadas, entre estas las BD NoSQL. Por ejemplo, hay sistemas que han generado exabytes de datos provenientes de sensores, con cientos de millones de datos espaciales, temporales y sociales [6].…”

Section: Introductionunclassified

Una comparación de rendimiento entre Oracle y MongoDB

Arboleda

Rendón

Vásquez

2016

Cien.Ing.Neogranadina

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<p>La creciente y enorme cantidad de datos, del orden de exabytes, generados por las aplicaciones empresariales actuales han originado conjuntos masivos de estos. Los sistemas de gestión de bases de datos (SGBD) NoSQL han surgido como una alternativa a los SGBD relacionales para la gestión de estos conjuntos. Entre los principales SGBD NoSQL está MongoDB. En este artículo se compara el rendimiento entre MongoDB y Oracle (uno de los principales SGBD que soporta bases de datos relacionales). La comparación se basa en las operaciones de inserción, consulta, actualización y borrado (CRUD, por sus siglas en inglés). Aunque se requieren experimentos más exhaustivos y muchos otros tipos de pruebas, los resultados ofrecen un punto de partida para el análisis de rendimiento en estos SGBD.</p>

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Text Analytics: the convergence of Big Data and Artificial Intelligence

Cited by 97 publications

References 17 publications

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

A Topic Modelling Analysis of Living Labs Research

Una comparación de rendimiento entre Oracle y MongoDB

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