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Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Review of Infrastructures to Process Big Multimedia Data

A Review of Infrastructures to Process Big Multimedia Data
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Author(s): Jaime Salvador (Universidad Central del Ecuador, Quito, Ecuador), Zoila Ruiz (Universidad Central del Ecuador, Quito, Ecuador) and Jose Garcia-Rodriguez (University of Alicante, Alicante, Spain)
Copyright: 2020
Pages: 12
Source title: Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch001


View A Review of Infrastructures to Process Big Multimedia Data on the publisher's website for pricing and purchasing information.


In the last years, the volume of information is growing faster than ever before, moving from small to huge, structured to unstructured datasets like text, image, audio and video. The purpose of processing the data is aimed to extract relevant information on trends, challenges and opportunities; all these studies with large volumes of data. The increase in the power of parallel computing enabled the use of Machine Learning (ML) techniques to take advantage of the processing capabilities offered by new architectures on large volumes of data. For this reason, it is necessary to find mechanisms that allow classify and organize them to facilitate to the users the extraction of the required information. The processing of these data requires the use of classification techniques that will be reviewed. This work analyzes different studies carried out on the use of ML for processing large volumes of data (Big Multimedia Data) and proposes a classification, using as criteria, the hardware infrastructures used in works of machine learning parallel approaches applied to large volumes of data.

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