Preventing drowning in information: A topic model approach to relating information on Strategic Scanning

Alexis Miranda Carillo, Edison Loza-Aguirre, Carlos Montenegro

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Information overload lead managers to not adequately use the relevant information they collect in Strategic Scanning. Such information comes in small pieces of that are dispersed in terms of time, language, and sources. These characteristics of information from Strategic Scanning usually prevent to identify connections with the previously collected information. In this paper, we propose an alternate tool for dealing with this issue by using topic analysis techniques. The tool provides a quick reading interface, in which the proximity relationship between various s can be easily visualized. We compare our tool with two mechanisms for clustering and proximity measurement. Our tool excelled in terms of execution time and of the pertinence of results.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1359-1364
Número de páginas6
ISBN (versión digital)9781728176246
DOI
EstadoPublicada - dic. 2020
Publicado de forma externa
Evento2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 - Las Vegas, Estados Unidos
Duración: 16 dic. 202018 dic. 2020

Serie de la publicación

NombreProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

Conferencia

Conferencia2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
País/TerritorioEstados Unidos
CiudadLas Vegas
Período16/12/2018/12/20

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