scholarly journals Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 190 ◽  
Author(s):  
Jonas R. Dourado ◽  
Jordão Natal de Oliveira Júnior ◽  
Carlos D. Maciel
Keyword(s):  
Social Media ◽  
Big Data ◽  
Transfer Entropy ◽  
Data Series ◽  
Data Parallelism ◽  
Task Parallelism ◽  
Hidden Information ◽  
Beowulf Cluster ◽  
Processing Power ◽  
Data Term

Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.

Big Data Ethics in Social Media-- A Case Study of Evaluating Websites

2020 ◽  
Vol 10 (1) ◽  
pp. 14-31
Author(s):  
Yueming Niu ◽  
Yulin Yao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Ethical Issues ◽  
Personal Information ◽  
Information Protection ◽  
Qualitative And Quantitative Analysis ◽  
Hidden Information ◽  
Qualitative And Quantitative ◽  
Data Ethics

This article combines qualitative and quantitative analysis to study the ethical issues of Big Data in social media, especially in evaluating websites. First, this article discusses the Big Data ethics of evaluation websites, and finds that there are some problems in the evaluation websites, such as false information, hidden information, and lack of user information protection. Second, this article uses questionnaires to investigate the awareness of users of different genders and ages on the evaluation website and their personal information protection consciousness.

DYNAMIC EXTRACTION AND ANALYTICS OF BIG DATA FROM CLOUD AND SOCIAL MEDIA INTEGRATED PLATFORMS

2020 ◽  
Vol 9 (6) ◽  
pp. 3703-3711
Author(s):  
N. Oberoi ◽  
S. Sachdeva ◽  
P. Garg ◽  
R. Walia
Keyword(s):  
Social Media ◽  
Big Data ◽  
Dynamic Extraction

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

scholarly journals Industrial revolution 4.0 on big data adoption factors on social media users and popularity brands

2021 ◽  
Vol 1839 (1) ◽  
pp. 012004
Author(s):  
W Sardjono ◽  
G Rama Putra ◽  
E Selviyanti ◽  
A Cholidin ◽  
G Salim
Keyword(s):  
Social Media ◽  
Big Data ◽  
Industrial Revolution ◽  
Adoption Factors
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