Big data analytics with graphical techniques applied on sensors data

The term "Big Data" is now a popular way to refer to massive digital information available in both structured and unstructured form integrated from multiple, diverse, dynamic sources of information. Big data can be used to solve a variety of problems with significant cost reduction cost. A...

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Vydáno v:IOP conference series. Materials Science and Engineering Ročník 591; číslo 1; s. 12063 - 12070
Hlavní autor: Oroian-Boca, M L
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.08.2019
Témata:
Big Data
Cost analysis
Data analysis
Datasets
Massive data points
Social networks
ISSN:1757-8981, 1757-899X
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Shrnutí:The term "Big Data" is now a popular way to refer to massive digital information available in both structured and unstructured form integrated from multiple, diverse, dynamic sources of information. Big data can be used to solve a variety of problems with significant cost reduction cost. Analysis and processing of large data sets represent a significant challenge. Massive data sets are collected and studied in numerous domains, from engineering sciences to social networks, biomolecular research, commerce, and security. Extracting valuable information from big data requires innovative approaches that efficiently process large amounts of data as well as handle and, moreover, utilize their big data analytics structure can be used in electronics industry too. In this study, I created a graphic tool in LabVIEW for the analyses of ECG signals for 3 types of subjects, in the same environment take in case the age of each subject and the influence of the environment on the physiological state of each one. The environmental conditions varied and the variations in the human body have been assessed in three situations: studying with music, studying without music and in relaxing state. LabVIEW proved to be an efficient tool to analyse and compare the produced signals in various time spans and also to compare the ECG signals for the investigated subjects.
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ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/591/1/012063