scholarly journals IoT-Enabled Big Data Analytics Architecture for Multimedia Data Communications

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Muhammad Babar ◽  
Mohammad Dahman Alshehri ◽  
Muhammad Usman Tariq ◽  
Fasee Ullah ◽  
Atif Khan ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
High Volume ◽  
Multimedia Systems ◽  
Multimedia Data ◽  
The Internet ◽  
Data Communications ◽  
Multimedia Big Data ◽  
Layered Architecture

The present spreading out of the Internet of Things (IoT) originated the realization of millions of IoT devices connected to the Internet. With the increase of allied devices, the gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, and control to resolve the QoS issues for multimedia data communications. However, it becomes challenging for multimedia systems to tackle the diverse multimedia-enabled IoT settings including healthcare, traffic videos, automation, society parking images, and surveillance that produce a massive amount of big multimedia data to be processed and analyzed efficiently. There are several challenges in the existing structural design of the IoT-enabled data management systems to handle MMBD including high-volume storage and processing of data, data heterogeneity due to various multimedia sources, and intelligent decision-making. In this article, an architecture is proposed to process and store MMBD efficiently in an IoT-enabled environment. The proposed architecture is a layered architecture integrated with a parallel and distributed module to accomplish big data analytics for multimedia data. A preprocessing module is also integrated with the proposed architecture to prepare the MMBD and speed up the processing mechanism. The proposed system is realized and experimentally tested using real-time multimedia big data sets from athentic sources that discloses the effectiveness of the proposed architecture.

Call for Special Issue Papers: Multimedia Big Data Analytics for Engineering Education

Big Data ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 87-88
Author(s):  
Priyan Malarvizhi Kumar ◽  
Hari Mohan Pandey ◽  
Gautam Srivastava
Keyword(s):  
Big Data ◽  
Engineering Education ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Special Issue ◽  
Multimedia Big Data

scholarly journals EXPRESS: “Get a £10 Free Bet Every Week!” – Gambling Advertising on Twitter: Volume, Content, Followers, Engagement and Regulatory Compliance

2021 ◽  
pp. 074391562199967
Author(s):  
Raffaello Rossi ◽  
Agnes Nairn ◽  
Josh Smith ◽  
Christopher Inskip
Keyword(s):  
Social Media ◽  
Big Data ◽  
Content Analysis ◽  
Volume Content ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Regulatory Compliance ◽  
The Internet ◽  
Policy Recommendations ◽  
Policy Makers

The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely hard to investigate using conventional techniques. In this paper the authors present three UK Twitter gambling advertising studies using both Big Data analytics and manual content analysis to explore the volume and content of gambling adverts, the age and engagement of followers, and compliance with UK advertising regulations. They analyse 890k organic adverts from 417 accounts along with data on 620k followers and 457k engagements (replies and retweets). They find that around 41,000 UK children follow Twitter gambling accounts, and that two-thirds of gambling advertising Tweets fail to fully comply with regulations. Adverts for eSports gambling are markedly different from those for traditional gambling (e.g. on soccer, casinos and lotteries) and appear to have strong appeal for children, with 28% of engagements with eSports gambling ads from under 16s. The authors make six policy recommendations: spotlight eSports gambling advertising; create new social-media-specific regulations; revise regulation on content appealing to children; use technology to block under-18s from seeing gambling ads; require ad-labelling of organic gambling Tweets; and deploy better enforcement.

scholarly journals Significance of big data analytics and the internet of things (IoT) aspects in industrial development, governance and sustainability

2020 ◽  
Vol 1 ◽  
pp. 107-111
Author(s):  
Daniel Lee Andersen ◽  
Christine Sarah Anne Ashbrook ◽  
Neil Bang Karlborg
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Industrial Development ◽  
Big Data Analytics ◽  
The Internet ◽  
The Internet Of Things

Analysis of Big Data Analytics in Healthcare Sector: Applications and Tools

2020 ◽  
Vol 17 (12) ◽  
pp. 5605-5612
Author(s):  
A. Kaliappan ◽  
D. Chitra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
High Volume ◽  
Healthcare Sector ◽  
Genome Database ◽  
Tremendous Amount ◽  
The Impact ◽  
Different Sources

In today’s world, an immense measure of information in the form of unstructured, semi-structured and unstructured is generated by different sources all over the world in a tremendous amount. Big data is the termed coined to address these enormous amounts of data. One of the major challenges in the health sector is handling a high-volume variety of data generated from diverse sources and utilizing it for the wellbeing of human. Big data analytics is one of technique designed to operate with monstrous measures of information. The impact of big data in healthcare field and utilization of Hadoop system tools for supervising the big data are deliberated in this paper. The big data analytics role and its theoretical and conceptual architecture include the gathering of diverse information’s such as electronic health records, genome database and clinical decisions support systems, text representation in health care industry is investigated in this paper.

Big Data Analytics in Bioinformatics

Biotechnology ◽  
2019 ◽  
pp. 1967-1984
Author(s):  
Dharmendra Trikamlal Patel
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Annual Growth ◽  
Predictive Analysis ◽  
The Internet ◽  
Data Generation ◽  
Intelligent Devices ◽  
The Internet Of Things

Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.

Urban planning and building smart cities based on the Internet of Things using Big Data analytics

2016 ◽  
Vol 101 ◽  
pp. 63-80 ◽  
Author(s):  
M. Mazhar Rathore ◽  
Awais Ahmad ◽  
Anand Paul ◽  
Seungmin Rho
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
The Internet ◽  
The Internet Of Things
Sign in / Sign up

Export Citation Format

Share Document