Big data analytics for critical information classification in online social networks using classifier chains

2022 ◽  
Author(s):  
Douglas H. Silva ◽  
Erick G. Maziero ◽  
Muhammad Saadi ◽  
Renata L. Rosa ◽  
Juan C. Silva ◽  
...  
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Online Social Networks ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Critical Information ◽  
Information Classification ◽  
Classifier Chains

Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks

Big Data ◽  
2016 ◽  
pp. 1403-1420 ◽  
Author(s):  
Yingxu Wang ◽  
Victor J. Wiebe
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Computational Intelligence ◽  
Data Analytics ◽  
Collective Intelligence ◽  
Big Data Analytics ◽  
Fuzzy Data ◽  
Web Based ◽  
Data Engineering ◽  
Computational Sociology

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.

scholarly journals Big Data Analytics With Reference To Wide Variety of Domains

2019 ◽  
Vol 8 (10) ◽  
pp. 1100-1104
Keyword(s):  
Health Care ◽  
Social Networks ◽  
Big Data ◽  
Data Analytics ◽  
High Speed ◽  
Big Data Analytics ◽  
Huge Amount ◽  
Government Sector ◽  
Big Data Technologies ◽  
Survey Results

In the current day scenario, a huge amount of data is been generated from various heterogeneous sources like social networks, business apps, government sector, marketing, health care system, sensors, machine log data which is created at such a high speed and other sources. Big Data is chosen as one among the upcoming area of research by several industries. In this paper, the author presents wide collection of literature that has been reviewed and analyzed. This paper emphasizes on Big Data Technologies, Application & Challenges, a comparative study on architectures, methodologies, tools, and survey results proposed by various researchers are presented

scholarly journals Cloud Based Big Data Analytics Framework for Face Recognition in Social Networks Using Machine Learning

2015 ◽  
Vol 50 ◽  
pp. 623-630 ◽  
Author(s):  
A. Vinay ◽  
Vinay S. Shekhar ◽  
J. Rituparna ◽  
Tushar Aggrawal ◽  
K.N. Balasubramanya Murthy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Big Data ◽  
Face Recognition ◽  
Data Analytics ◽  
Big Data Analytics

Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks

2014 ◽  
Vol 8 (3) ◽  
pp. 29-44 ◽  
Author(s):  
Yingxu Wang ◽  
Victor J. Wiebe
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Data Analytics ◽  
Collective Intelligence ◽  
Big Data Analytics ◽  
Fuzzy Data ◽  
Web Based ◽  
Fuzzy Models ◽  
Data Engineering ◽  
Computational Sociology

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.

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