scholarly journals BIG DATA AS AN ECONOMIC PHENOMENON: THEORETICAL AND METHODOLOGICAL SUMMARY

2021 ◽  
pp. 132-139
Author(s):  
M. Stashevskaya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Economic Activity ◽  
Large Scale ◽  
Digital Technologies ◽  
Digital Economy ◽  
Descriptive Model ◽  
Back Ground ◽  
Definition Of

The article contains a study of existing views on the economic content of big data. From among the views, within which the authors define big data, the descriptive-model, utility-digital and complex-technological approaches are formulated. Against the back- ground of the large-scale spread of digital technologies (machine learning, cloud computing, artificial intelligence, augmented and virtual reality, etc.), functioning thanks to big data, the study of their economic essence is becoming especially relevant. As a result, it was found that the basis of economic activity in the digital economy is big data. The definition of big data as a resource of the digital economy is proposed.

Prospects for Cooperation Between Russia and the Republic of Korea in the Fields of Big Data, Network and Artificial Intelligence for the Development of the Digital Economy

2021 ◽  
Vol 65 (8) ◽  
pp. 51-60
Author(s):  
Yujeong Kim
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Large Scale ◽  
Technology Development ◽  
Technological Development ◽  
Digital Economy ◽  
Republic Of Korea ◽  
Data Network ◽  
Leading Position ◽  
The Government

Today, each country has interest in digital economy and has established and implemented policies aimed at digital technology development and digital transformation for the transition to the digital economy. In particular, interest in digital technologies such as big data, 5G, and artificial intelligence, which are recognized as important factors in the digital economy, has been increasing recently, and it is a time when the role of the government for technological development and international cooperation becomes important. In addition to the overall digital economic policy, the Russian and Korean governments are also trying to improve their international competitiveness and take a leading position in the new economic order by establishing related technical and industrial policies. Moreover, Republic of Korea often refers to data, network and artificial intelligence as D∙N∙A, and has established policies in each of these areas in 2019. Russia is also establishing and implementing policies in the same field in 2019. Therefore, it is timely to find ways to expand cooperation between Russia and Republic of Korea. In particular, the years of 2020and 2021marks the 30th anniversary of diplomatic relations between the two countries, and not only large-scale events and exchange programs have prepared, but the relationship is deepening as part of the continued foreign policy of both countries – Russia’s Eastern Policy and New Northern Policy of Republic of Korea. Therefore, this paper compares and analyzes the policies of the two countries in big data, 5G, and artificial intelligence to seek long-term sustainable cooperation in the digital economy.

scholarly journals Digital Economy and Anti-Corruption: New Digital Models

2019 ◽  
Vol 71 ◽  
pp. 03004
Author(s):  
E.L. Sidorenko ◽  
A.A. Lykov
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Crime Prevention ◽  
Digital Technologies ◽  
Digital Economy ◽  
Digital Models ◽  
Research Results ◽  
Corruption Prevention

The authors of this paper consider promising areas of the corruption prevention using the latest digital technologies: Blockchain, Internet of Things, Artificial Intelligence and Big Data. The purpose of this research is the analysis of advantages of the digital economy development in terms of solving social problems and crime prevention. The authors also show functional digital models of the anti-corruption compliance are defined. In addition, the research results include the determination of some shortcomings of the proposed models associated with the imperfection of the current legislation.

DEVELOPMENT TENDENCIES OF THE DIGITAL ECONOMY

2021 ◽  
Vol 4 (4) ◽  
pp. 70-75
Author(s):  
Shoxida Mannonova ◽  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Digital Technologies ◽  
Educational Programs ◽  
Digital Economy ◽  
Skilled Labor ◽  
Professional Skills ◽  
Effective Use

The article analyzes the features of the digital economy, the ecosystem, the effective use of digital technologies in the development of the economy and current issues in this area, as well as the necessary skills of training for the digital economy, modern professions that will be needed in the near future.Keywords:digital economy, big data, artificial intelligence, nanotechnology, quantum technologies, Internet of things, ecosystem, personnel, professional skills, skilled labor, businessmodels, joint educational programs

scholarly journals Digital economy as a factor in increasing the competitiveness of countries and industries: a quantitative analysis

2021 ◽  
Vol 188 (3-4) ◽  
pp. 69-76
Author(s):  
Elena Sergushina ◽  
◽  
Dmitriy Leontyev ◽  
Oxana Kozhukalova ◽  
Irina Dambayeva ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quantitative Analysis ◽  
Business Models ◽  
Digital Technologies ◽  
Digital Economy ◽  
Large Databases ◽  
The World ◽  
The Russian Federation ◽  
National Economies

The paper touches upon various aspects of the digital economy and its stages of its formation. The authors of the research present relevant data on the development of the digital economy both in the world and the Russian Federation, and forecast some of the consequences of the introduction of digital technologies based on quantitative analysis and modeling. The authors have analyzed several business models that appeared after the spread of large databases, artificial intelligence, machine learning, and other digital technologies. Based on the analysis as of 2020, it has been concluded that transition to digital technologies enhances the competitiveness of national economies.

scholarly journals Listening without ears: Artificial intelligence in audio mastering

2018 ◽  
Vol 5 (2) ◽  
pp. 205395171880855 ◽  
Author(s):  
Thomas Birtchnell
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Scientific Knowledge ◽  
Music Industry ◽  
Digital Technologies ◽  
The Past ◽  
Listening Environments ◽  
Pragmatic Strategies

Since the inception of recorded music there has been a need for standards and reliability across sound formats and listening environments. The role of the audio mastering engineer is prestigious and akin to a craft expert combining scientific knowledge, musical learning, manual precision and skill, and an awareness of cultural fashions and creative labour. With the advent of algorithms, big data and machine learning, loosely termed artificial intelligence in this creative sector, there is now the possibility of automating human audio mastering processes and radically disrupting mastering careers. The emergence of dedicated products and services in artificial intelligence-driven audio mastering poses profound questions for the future of the music industry, already having faced significant challenges due to the digitalization of music over the past decades. The research reports on qualitative and ethnographic inquiry with audio mastering engineers on the automation of their expertise and the potential for artificial intelligence to augment or replace aspects of their workflows. Investigating audio mastering engineers' awareness of artificial intelligence, the research probes the importance of criticality in their labour. The research identifies intuitive performance and critical listening as areas where human ingenuity and communication pose problems for simulation. Affective labour disrupts speculation of algorithmic domination by highlighting the pragmatic strategies available for humans to adapt and augment digital technologies.

scholarly journals Data Security and Privacy-Preserving Framework Using Machine Learning and Blockchain in Big-Data to Data Middle Platform in the Era of IR 4.0

2021 ◽  
Author(s):  
Chen Chuqiao ◽  
S.B. Goyal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Security And Privacy ◽  
Fourth Industrial Revolution ◽  
Data Life Cycle ◽  
Suitable Framework ◽  
Distributed Cloud

The modem data is collected by using IoT, stored in distributed cloud storage, and issued for data mining or training artificial intelligence. These new digital technologies integrate into the data middle platform have facilitated the progress of industry, promoted the fourth industrial revolution. And it also has caused challenges in security and privacy-preventing. The privacy data breach can happen in any phase of the Big-Data life cycle, and the Data Middle Platform also faces similar situations. How to make the privacy avoid leakage is exigency. The traditional privacy-preventing model is not enough, we need the help of Machine-Learning and the Blockchain. In this research, the researcher reviews the security and privacy-preventing in Big-Data, Machine Learning, Blockchain, and other related works at first. And then finding some gaps between the theory and the actual work. Based on these gaps, trying to create a suitable framework to guide the industry to protect their privacy when the organization contribute and operate their data middle platform. No only academicians, but also industry practitioners especially SMEs will get the benefit from this research.

Impact of Supply Chain Digitalisation on a Connected Global Market

2020 ◽  
pp. 186-204
Author(s):  
Farooq Habib ◽  
Murtaza Farooq Khan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Final Section ◽  
Digital Technologies ◽  
Global Market ◽  
The Third ◽  
The Impact

This chapter focuses on the impact of supply chain digitalisation on a connected global market. The first section focuses on the dynamic consumer requirements and preferences. The second section appraised the segmentation and mapping of digital technologies. The third section examines the contemporary application of digital technologies including: big data, blockchains, artificial intelligence, machine learning, and data analytics. The final section analysises the rules and regulations the form the basis of a contemporary framework for the governance of digital technologies.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Innovative Approach to Build a Scalable Hybrid Model for Prescriptive Maintenance

2021 ◽  
Author(s):  
Richard Büssow ◽  
Bruno Hain ◽  
Ismael Al Nuaimi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Hybrid Model ◽  
Domain Knowledge ◽  
Spare Parts ◽  
Good Prediction ◽  
First Principle ◽  
Plant Data ◽  
Rotating Equipment

Abstract Objective and Scope Analysis of operational plant data needs experts in order to interpret detected anomalies which are defined as unusual operation points. The next step on the digital transformation journey is to provide actionable insights into the data. Prescriptive Maintenance defines in advance which kind of detailed maintenance and spare parts will be required. This paper details requirements to improve these predictions for rotating equipment and show potential to integrate the outcome into an operational workflow. Methods, Procedures, Process First principle or physics-based modelling provides additional insights into the data, since the results are directly interpretable. However, such approaches are typically assumed to be expensive to build and not scalable. Identification of and focus on the relevant equipment to be modeled in a hybrid model using a combination of first principle physics and machine learning is a successful strategy. The model is trained using a machine learning approach with historic or current real plant data, to predict conditions which have not occurred before. The better the Artificial Intelligence is trained, the better the prediction will be. Results, Observations, Conclusions The general aim when operating a plant is the actual usage of operational data for process and maintenance optimization by advanced analytics. Typically a data-driven central oversight function supports operations and maintenance staff. A major lesson-learned is that the results of a rather simple statistical approach to detect anomalies fall behind the expectations and are too labor intensive. It is a widely spread misinterpretation that being able to deal with big data is sufficient to come up with good prediction quality for Prescriptive Maintenance. What big data companies are normally missing is domain knowledge, especially on plant critical rotating equipment. Without having domain knowledge the relevant input into the model will have shortcomings and hence the same will apply to its predictions. This paper gives an example of a refinery where the described hybrid model has been used. Novel and Additive Information First principle models are typically expensive to build and not scalable. This hybrid model approach, combining first principle physics based models with artificial intelligence and integration into an operational workflow shows a new way forward.

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