scholarly journals Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 15679-15701 ◽  
Author(s):  
Tasneem S. J. Darwish ◽  
Kamalrulnizam Abu Bakar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Intelligent Transportation ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Critical Issues

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.

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.

scholarly journals An Optimal Enhancement of the Dynamic Features of Recommender Systems

2019 ◽  
Vol 8 (2S4) ◽  
pp. 51-55
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Recommender Systems ◽  
Data Analytics ◽  
Recommendation Systems ◽  
Big Data Analytics ◽  
The Internet ◽  
User Requirements ◽  
Dynamic Features ◽  
Dynamic Nature

Recommendation systems come under the domain of Data mining and Big Data analytics. It is useful tool that is used to predict the ratings or preferences of a user from a pool of resources. The preferences of user are dynamic in nature. The immeasurable usage of internet is having a great impact on the way we deal our lives and communicate with each other. As a result, the requirements of user browsing the internet are changing radically. Recommender Systems (RSs) provide a technology that helps users in finding relevant or preferential information among the pool of information using internet. This paper puts forward not only the issues related to the dynamic nature of user’ requirements but also the changes in the systems’ contents. The Recommendation Systems which involves the above stated issues are termed as Dynamic Recommender Systems (DRSs). This paper first defines the concept of DRS and then explores the various parameters that is taken into account in developing a DRS. This paper also discusses the scope of contributions in this field and concludes citing in possible extensions that can improve the dynamic qualities of recommendation systems in future.

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