Advertising

2017 ◽  
Author(s):  
Mara Einstein
Keyword(s):  
Big Data ◽  
Digital Age ◽  
Daily Basis ◽  
Global Society ◽  
Editorial Content ◽  
Youtube Videos ◽  
Tv Commercials ◽  
Marketing Messages ◽  
Commercial Messages

3000. That's the number of marketing messages the average American confronts on a daily basis from TV commercials, magazine and newspaper print ads, radio commercials, pop-up ads on gaming apps, to pre-roll on YouTube videos and native advertising on mobile news apps. These commercial messages are so pervasive that we cannot help but be affected by perpetual come-ons to keeping buying. Over the last decade, advertising has become more devious, more digital, and more deceptive, with an increasing number of ads designed to appear to the untrained eye to be editorial content. It's easy to see why. As we have become smarter at avoiding ads, advertisers have become smarter about disguising them. Mara Einstein exposes how our shopping, political and even dating preferences are unwittingly formed by brand images and the mythologies embedded in them. Advertising: What Everyone Needs to Know® helps us combat the effects of manipulative advertising, and enables the reader to understand how marketing industries work in the digital age, particularly in their uses and abuses of Big Data. Most importantly, it awakens us to advertising's subtle and not so subtle impact on our lives-both as individuals and as a global society. What ideas and information are being communicated to us-and to what end?

Analysis of time series using Big Data algorithms

2021 ◽  
pp. 118-124
Author(s):  
А.И. Сотников
Keyword(s):  
Time Series ◽  
Big Data ◽  
Daily Basis ◽  
Processing Methods ◽  
Traditional Technologies ◽  
New Processing ◽  
Analysis Of Time Series

То, с какой скоростью человечество накапливает информацию ежедневно, и непредсказуемость завтрашнего дня показывают, что для прогнозирования временных рядов больших данных уже не хватает традиционных технологий и необходимы новые методы обработки. В связи с этим встает вопрос, какие методы возможно использовать в настоящее время для получения достоверного прогнозирования временных рядов больших данных? The speed at which humanity is accumulating information on a daily basis and the unpredictability of tomorrow show that traditional technologies are no longer enough for forecasting big data time series and new processing methods are needed. In this regard, the question arises, what methods can be used at present to obtain reliable forecasting of time series of big data?

Through the Time Barrier: Art and Design in the Digital Age

APRIA Journal ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 35-50
Author(s):  
Marijke Goeting
Keyword(s):  
Big Data ◽  
Fundamental Problem ◽  
Digital Age ◽  
Fast Computation ◽  
Process Data ◽  
Art And Design ◽  
The Past ◽  
Works Of Art ◽  
The World ◽  
Time Frames

During the past decade, computers have broken through the barrier of human time. Today, computers can process data in milli-, micro- and even nanoseconds and can (inter) act autonomously in time frames that exceed our capacity to perceive and respond to. This produces a fundamental problem – a gap between human time and the time of computers – and raises important questions: how do big data and fast computation affect our experience and understanding of time? If a computer is able to deal with the world faster than we can, are we doomed to live forever in the past, however near the present? Or are we dealing with a technological extension of the present, and how might we be able to understand and experience this? By analysing theory and works of art, this text examines how to deal with the shock produced by microtemporal technologies.

Some Peculiarities of Big Data Legal Regulation in the Digital Age

2021 ◽  
pp. 24-32
Author(s):  
A. A. Andreasyan ◽  
Artem Balyakin ◽  
Marina Nurbina ◽  
S. B. Taranenko
Keyword(s):  
Big Data ◽  
Digital Age ◽  
Legal Regulation

scholarly journals China’s E-Economy: An overview of Opportunities and Threats

2019 ◽  
Vol 5 (2) ◽  
pp. 74
Author(s):  
Qiao Yao
Keyword(s):  
Big Data ◽  
Capital Investment ◽  
Digital Age ◽  
Autonomous Driving ◽  
Industrial Sector ◽  
Venture Capital Investment ◽  
Economic Threat ◽  
The World ◽  
The Usa ◽  
The Impact

China is the world biggest country in terms of population. It has the highest number of internet and mobile users. The world most substantial labor forces reside in China. A large proportion of the world is dependent on its exports. Chinas economy grew, in the last decade because of its exports, it got attention all over the world. Economy experts consider China as an economic threat to the USA. However, more studies are mainly focused on China populations, Exports, and labor focus because of the high quantity. The dynamics of the economy has changed in the last decade because of internet penetration across the globe. The Chinas role in digital aspects is least studied. Therefore this paper has focused on providing an overview of E-economy of China. Through literature and world-leading financial and consultancy firms reports it has been observed that just like other aspects of the economy, the e-economy of China is also growing. Today in 2019 where more than 50% of the world has access to the internet, It is considered that the Silicon Valley of USA is deriving the digital age because all big tech companies are located in the USA. USA main exports are Internet-related or Tech products. It is a fact that the USA E-economy contributes more to GDP compared to China. However, China has a potentially bright future in this area and can be the leading country in technology. Exploring the future possibilities, the opportunities which China has to grow in the digital age, the researchers found already there are areas in digital aspects where China has to outnumber the USA. For instance, the Fintech China got more Capital venture investments in 2016 compared to the USA. China is the world second country after the USA in attracting venture capital investment for Virtual Reality, Autonomous Driving, Wearables technologies, Education Technology, Robotics and drones, and 3D Printing. China is in the third position in terms of attracting investment for big data and artificial intelligence. The study concludes that China needs to focus more on big data and AI to continue its growth.  The growing digitalization can improve agriculture and industrial activities as the economy is maturing. The paper is useful for digital experts to view the understand the e-economy in depth, future researchers can narrow down the topic to observe the impact of E-economy on agriculture and industrial sector.

Big Data Custodianship in a Global Society

2014 ◽  
Vol 34 (1) ◽  
pp. 109-116 ◽  
Author(s):  
Chris Poulin
Keyword(s):  
Big Data ◽  
Global Society

scholarly journals Law in Context for the Digital Age

2019 ◽  
Vol 36 (1) ◽  
pp. 3-11
Author(s):  
Pompeu Casanovas ◽  
Jianfu Chen ◽  
David Wishart
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Digital Age ◽  
The Internet ◽  
Historical Account ◽  
Web Of Data ◽  
The Law ◽  
The Internet Of Things ◽  
The Web

We introduce both the new inception of Law in Context - A Socio-legal Journal and the continuing issue of LiC 36 (1). The editorial provides a brief historical account of the Journal since its inception in the early 1980s, in the context of the evolution of the Law & Society movement. It also describes the changes produced in the digital age by the emergence of the Web of Data, Big Data, and the Internet of Things. The convergence between Law & Society and Artificial Intelligence & Law is also discussed. Finally, we introduce briefly the articles included in this issue.          

scholarly journals An Efficient Classification of Intrusion in Bigdata based on Chaos Game Optimization and Ensemble SVM

2021 ◽  
Author(s):  
A Ponmalar ◽  
V Dhanakoti
Keyword(s):  
Big Data ◽  
Classification Accuracy ◽  
High Speed ◽  
Performance Metrics ◽  
Daily Basis ◽  
Network Services ◽  
Detection Methods ◽  
Svm Model ◽  
Chaos Game ◽  
Big Data Classification

Abstract The growing popularity of the internet and network services has resulted in an increase in data in all fields. The data are increasing on the daily basis with high speed. This also creates some daunting issues such as security, storage, and so on. Meanwhile, the detection of intrusion from the big data in the ultra-high-speed environment is a critical task. Several intrusion detection methods are carried out to classify the big data based on intrusion and without intrusion. The optimum accuracy of big data classification, however, has yet to be achieved. Hence we proposed a novel ensemble SVM Model, in which the ensemble SVM is incorporated with the Chaos Game Optimization (CGO) algorithm, which can be exploited to enhance the classification accuracy. Our method also classifies the intrusion based on their types. It also classifies almost nine attacks as, Exploits, DoS, Backdoor, Generic, Worms, Analysis, Fuzzers, Shellcode, Reconnaissance. The experimental analysis is carried on the UNSW-NB15 big data dataset. The performance metrics precision, accuracy, recall, F-score are analyzed and compared with the state-of-art works such as BAMS-OIF, SAD, SMLsmBDA, and BDPM. The outcomes depict that the proposed work outperforms all the other existing works in terms of classification accuracy.

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