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A Review of Infrastructures to Process Big Multimedia Data

A Review of Infrastructures to Process Big Multimedia Data

Jaime Salvador, Zoila Ruiz, Jose Garcia-Rodriguez
Copyright: © 2020 |Pages: 12
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch001
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MLA

Salvador, Jaime, et al. "A Review of Infrastructures to Process Big Multimedia Data." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1-12. https://doi.org/10.4018/978-1-7998-2460-2.ch001

APA

Salvador, J., Ruiz, Z., & Garcia-Rodriguez, J. (2020). A Review of Infrastructures to Process Big Multimedia Data. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 1-12). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch001

Chicago

Salvador, Jaime, Zoila Ruiz, and Jose Garcia-Rodriguez. "A Review of Infrastructures to Process Big Multimedia Data." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1-12. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch001

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Abstract

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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