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Chapitre D'ouvrage Année : 2017

Mining Unstructured Big Data for Competitive Intelligence and Business Intelligence

Résumé

In the information era, people’s lives are deeply impacted by IT due to exposure to social networks, emails, RSS feeds, chats, white papers, web pages, etc. Such data are considered very valuable for companies nowadays since they will help them improve their strategies, for example, by analyzing their customers’ trends or their competitors’ marketing interventions. Several decisional tools have been developed in the past few years, but they still faced a big challenge. Being mostly based on relational databases, it was difficult to use them to analyze unstructured data, which represent nowadays more than 85% of the available data. Thus, there is a rising need for a suitable management process for unstructured data through collecting, managing, transferring, and transforming them into meaningful informed data. In this chapter, we explore a new analytical model of big unstructured data for the competitive intelligence system XEW.
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Dates et versions

hal-03131904 , version 1 (04-02-2021)

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  • HAL Id : hal-03131904 , version 1

Citer

Amine El Haddadi, Anass El Haddadi, Zakaria Boulouard, Fadwa Bouhafer, Nihal Chouati, et al.. Mining Unstructured Big Data for Competitive Intelligence and Business Intelligence. Vijayan Sugumaran
; Arun Kumar Sangaiah; Arunkumar Thangavelu. Computational Intelligence and Applications in Business Intelligence and Big Data Analytics, Taylor & Francis eBooks, 2017, 978-1315180748. ⟨hal-03131904⟩
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