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Big Data Spatial Analytics in Social Networks using Hadoop

by Sultan Alenezi, Saleh Mesbah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 14
Year of Publication: 2015
Authors: Sultan Alenezi, Saleh Mesbah
10.5120/ijca2015906745

Sultan Alenezi, Saleh Mesbah . Big Data Spatial Analytics in Social Networks using Hadoop. International Journal of Computer Applications. 128, 14 ( October 2015), 21-26. DOI=10.5120/ijca2015906745

@article{ 10.5120/ijca2015906745,
author = { Sultan Alenezi, Saleh Mesbah },
title = { Big Data Spatial Analytics in Social Networks using Hadoop },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 14 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 21-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number14/22942-2015906745/ },
doi = { 10.5120/ijca2015906745 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:22:03.849815+05:30
%A Sultan Alenezi
%A Saleh Mesbah
%T Big Data Spatial Analytics in Social Networks using Hadoop
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 14
%P 21-26
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A lot of applications in different fields generate huge data streams, known as Big Data. These kinds of applications needs special systems of data analytics in order to collect, store and process these big data files, like Hadoop framework. This paper aims to analyze social media big data to identify the widespread of certain keywords. A Java-Hadoop application is developed to analyze data obtained from Twitter social network. The application is used to identify number of people (Tweets) who mentioned specific medical keywords (e.g. Cancer) classified by location. The application is developed using Java Eclipse on CentOS Linux operating System and runs on Oracle Virtual Machine. The analysis aims to help in decision making according to the number of people tweeting about cancer or any related word (like tumor) and analyze them according to their cities all over the world. The results are used to create a GIS layer to spatially enhance the visualization of the obtained results.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Big Data Analytics Twitter Hadoop Social Network Cancer CentOS.

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