Big Data Analytics and Machine Learning for Industry 4.0: An Overview

Le, Nguyen Tuan Thanh and Pham, Manh Linh (2021) Big Data Analytics and Machine Learning for Industry 4.0: An Overview. In: Industry 4.0 Interoperability, Analytics, Security, and Case studies. Big Data for Industry 4.0: Challenges and Applications . CRC Press - Taylor & Francis Group, LLC, Boca Raton, FL, USA, pp. 1-11. ISBN 9781003048855

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Official URL: https://doi.org/10.1201/9781003048855

Abstract

The concept of “Big data” was mentioned for the first time by Roger Mougalas in 2005. Volume hints to the size and/or scale of datasets. Until now, there is no universal threshold for data volume to be considered as big data, because of the time and diversity of datasets. Velocity indicates the speed of processing data. It can fall into three categories: streaming processing, real-time processing, or batch processing. Value alludes to the usefulness of data for decision making. Veracity denotes the quality and trustworthiness of datasets. Parallelization allows one to improve computation time by dividing big problems into smaller instances, distributing smaller tasks across multiple threads and then performing them simultaneously. Feature selection is useful for preparing high scale datasets. Sampling is a method for data reducing that helps to derive patterns in big datasets by generating, manipulating, and analyzing subsets of the original data.

Item Type:	Book Section
Uncontrolled Keywords:	Big Data Analytics, Industry 4.0, Machine Learning, Deep Learning
Subjects:	Information Technology (IT)
Divisions:	Center of Multidisciplinary Integrated Technologies for Field Monitoring (FIMO) Faculty of Information Technology (FIT)
Depositing User:	Dr. Mạnh Linh Phạm
Date Deposited:	15 Mar 2021 04:23
Last Modified:	15 Mar 2021 04:23
URI:	http://eprints.uet.vnu.edu.vn/eprints/id/eprint/4374

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Big Data Analytics and Machine Learning for Industry 4.0: An Overview. (deposited 20 Aug 2020 04:31)
- Big Data Analytics and Machine Learning for Industry 4.0: An Overview. (deposited 15 Mar 2021 04:23) [Currently Displayed]

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