Art Innovative Systems for Value Tagging

2021 ◽  
pp. 1163-1174
Author(s):  
Laurel Powell ◽  
Anna Gelich ◽  
Zbigniew W. Ras
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Data Analytics ◽  
Word Of Mouth ◽  
Big Data Analytics ◽  
Art Market ◽  
Fine Art ◽  
The Internet ◽  
User Needs ◽  
Personalized Recommender System

Prices of artworks are rather arbitrary. Artists use word of mouth and galleries to learn about pricing. Also, the professionals in the art market are searching the internet for information about prices of comparable artworks to the ones they plan to sell, but it is not very helpful. Existing systems do not use data analytics but human experts to evaluate fine art pieces and make recommendations. The system discussed in this article, called ArtIST, is based on big data analytics. Using the artist's name, appraisal of the piece of art is done by a personalized recommender system built from the data describing similar artists and similar art pieces including information about their sales. To evaluate an art piece using ArtIST, the user needs to submit the same information about the work as is required by existing art appraisal tools or websites.

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.

Privacy towards GIS Based Intelligent Tourism Recommender System in Big Data Analytics

2019 ◽  
pp. 81-100
Author(s):  
Abhaya Kumar Sahoo ◽  
Chittaranjan Pradhan ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Data Analytics ◽  
Big Data Analytics

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.

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