scholarly journals BIG DATA TELECOMMUNICATION COMPANY TOOLS TO ENHANCE DECISION-MAKING EFFICIENCY IN COMPLEX ECONOMIC SYSTEMS

2021 ◽  
Author(s):  
Nataliia Geseleva ◽  
Anastasiia Yaroslavtseva
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Customer Service ◽  
Mobile Communication ◽  
Telecommunications Industry ◽  
World Market ◽  
Global Trends ◽  
The World ◽  
Very Large Datasets ◽  
Big Data Technologies

The paper examines the telecommunications industry, its development and impact on economic growth in countries including Ukraine. The characteristics of mobile communication, as a segment of the telecommunications industry that is most actively progressing, both in the world as a whole and in Ukraine, are given. It’s examined a current state of the Ukrainian mobile communication market. Its importance for the national economy is reviewed. The Ukrainian mobile market has been studied; the changes that have taken place in recent years in the direction of global trends in the field of communications. Development trends that encourage mobile operators to develop their own platforms, introduce new products and services are considered. Examples of current developments and services of operators such as virtual mobile automatic telephone exchange, Big Data Scoring, Vodafone Analytics and others are given. The article pays special attention to Big Data processing and analysis technologies. Big data is defined as very large datasets that can be analyzed computationally to reveal patterns, trends, and associations – especially in connection with human behavior and interactions. A big data revolution has arrived with the growth of the Internet, wireless networks, smartphones, social media and other technology. These features of Big Data are the ability to use Data Mining. Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. Data mining depends on effective data collection, warehousing, and computer processing. Data mining processes are used to build machine learning models that power applications including search engine technology and website recommendation programs. Also describes how Big Data affects the retail industry, namely helping to optimize merchandising tactics, personalize customer service, increase advertising effectiveness, target offline shoppers (remarketing) and expand cross-selling. Also in the field of telecommunications, Big Data helps providers to automate and optimize the provision of their services. Thus, the introduction of Big Data technologies will allow Ukraine to become a more competitive country on the world market.

Big Data: Technologies, Challenges, Data Analytics and Management: A Review

2017 ◽  
Vol 12 (01) ◽  
Author(s):  
Shweta Kaushik
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
High Speed ◽  
Information Base ◽  
Information Order ◽  
The World ◽  
Big Data Technologies ◽  
Business Profitability ◽  
The Web

Internet assumes an essential part in giving different learning sources to the world, which encourages numerous applications to give quality support of the customers. As the years go on the web is over-burden with parcel of data and it turns out to be difficult to extricate the applicable data from the web. This offers path to the advancement of the Big Data and the volume of the information continues expanding quickly step by step. Enormous Data has increased much consideration from the scholarly world and the IT business. In the advanced and figuring world, data is produced and gathered at a rate that quickly surpasses the limit go. Data mining procedures are utilized to locate the concealed data from the huge information. This Technique is utilized store, oversee, and investigate high speed of information and this information can be in any shape organized or unstructured frame. It is hard to handle substantial volume of information utilizing information base strategy like RDBMS. From one perspective, Big Data is amazingly important to deliver efficiency in organizations and transformative achievements in logical controls, which give us a considerable measure of chances to make incredible advances in many fields. There is most likely the future rivalries in business profitability and advances will without a doubt merge into the Big Data investigations. Then again, Big Data likewise emerges with many difficulties, for example, troubles in information catch, information stockpiling, information investigation and information perception. In this paper we concentrate on the audit of Big Data, its information order techniques and the way it can be mined utilizing different mining strategies.

Digital Marketing and E-Tailing Technological Innovations

2020 ◽  
pp. 37-65
Author(s):  
Alan D. Smith
Keyword(s):  
Data Mining ◽  
Quality Assurance ◽  
Big Data ◽  
Information Retrieval ◽  
Customer Service ◽  
Middle Level ◽  
Purchase Decision ◽  
Product Reviews ◽  
Privacy Concerns ◽  
Web Presence

In an age of public mistrust of the most basic institutions, businesses are not exempted. Essentially all e-tailers want to deliver personalized and real-time communications to customers that are tailored to their interests and preferences, and are based on big data mining that customers will value over privacy concerns. This is an era in which e-commerce retailers continue to dominate the marketplace and it is integral that consumers are able to trust the manufacturers, retailers, and the service/product reviews that they read online. Such trust is particularly important if their ultimate purchase decision is a successful one. A survey of middle-level managers was analyzed to identity the basic elements: e-personalization, namely online purchasing behaviors, personalized communications, information-retrieval services, degree of personal web presence, quality assurance of customer service, and the promotion of customization services. These elements were found to be conceptually and statistically related to retailer benefits of increased buying and customer loyalty.

scholarly journals Big Data Applications in Vaccinology

2021 ◽  
Vol 6 (2) ◽  
pp. 59-80
Author(s):  
Joseph E. Kasten
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Human Race ◽  
Big Data Applications ◽  
The World ◽  
Big Data Technologies ◽  
History Of ◽  
Structured Literature Review ◽  
Over Time

The development of vaccines has been one of the most important medical and pharmacological breakthroughs in the history of the world. Besides saving untold lives, they have enabled the human race to live and thrive in conditions thought far too dangerous only a few centuries ago. In recent times, the development of the COVID-19 vaccine has captured the world’s attention as the primary tool to defeat the current pandemic. The tools used to develop these vaccines have changed dramatically over time, with the use of big data technologies becoming standard in many instances. This study performs a structured literature review centered on the development, distribution, and evaluation of vaccines and the role played by big data tools such as data analytics, datamining, and machine learning. Through this review, the paper identifies where these technologies have made important contributions and in what areas further research is likely to be useful.

Mining Big Data and Streams

2018 ◽  
pp. 406-417
Author(s):  
Hoda Ahmed Abdelhafez
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Large Datasets ◽  
Lessons Learned ◽  
Velocity Data ◽  
Time Data ◽  
Data Mining Techniques ◽  
Complex Information ◽  
Very Large Datasets ◽  
Data Volume

Mining big data is getting a lot of attention currently because the businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety and velocity, data mining techniques and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.

Mining Big Data and Streams

2019 ◽  
pp. 94-107
Author(s):  
Hoda Ahmed Abdelhafez
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Large Datasets ◽  
Lessons Learned ◽  
Velocity Data ◽  
Time Data ◽  
Data Mining Techniques ◽  
Complex Information ◽  
Very Large Datasets ◽  
Data Volume

Mining big data is getting a lot of attention currently because businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety, and velocity, data mining techniques, and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.

Data Mining Problems Classification and Techniques

2020 ◽  
pp. 70-93
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Retrieval ◽  
Data Analytics ◽  
Complex Data ◽  
Healthcare Industry ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
Hidden Knowledge

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

Exploring the role of website quality and hedonism in the formation of e-satisfaction and e-loyalty

2017 ◽  
Vol 11 (3) ◽  
pp. 246-267 ◽  
Author(s):  
Asad Ahmad ◽  
Obaidur Rahman ◽  
Mohammed Naved Khan
Keyword(s):  
Factor Analysis ◽  
Customer Service ◽  
Structural Equation ◽  
World Market ◽  
Content Type ◽  
Internet Users ◽  
Online Retailers ◽  
The World ◽  
Major Factors

Purpose The purpose of this study is to explore the factors that help in building e-loyalty towards online retailers. Internet has brought the world market into a single platform. Marketers have started using Internet as a new and innovative way to interact and reach people all around the world. With the increase in the number of Internet users, the number of e-marketers has also increased. In the context of online retailing, the service quality being offered is increasingly being used as a tool for competitive advantage. E-tailers are embracing superior e-services to attract, retain and convert patrons into loyal customers. Design/methodology/approach The researchers in the present study have used a research instrument that consists of constructs of the modified eTailQ scale, hedonism and e-satisfaction that together result in the formation of e-loyalty. Researcher-controlled sampling was employed to collect data from 159 student respondents. Findings Exploratory factor analysis, confirmatory factor analysis and structural equation modelling were applied for analysing the collected data. The results of the study demonstrate that major factors which help in the formation of e-loyalty are e-satisfaction, customer service, privacy and hedonism. Originality/value This study extends the understanding of the role of e-satisfaction, customer service, privacy and hedonism in the formation of loyal consumers. The researchers proposed a model to study the factors impacting the e-loyalty of the Internet shoppers in India. The findings of the study are expected to help both researchers and marketers.

Data Mining Problems Classification and Techniques

2018 ◽  
Vol 3 (1) ◽  
pp. 38-57 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Retrieval ◽  
Data Analytics ◽  
Complex Data ◽  
Healthcare Industry ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
Hidden Knowledge

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

scholarly journals RESEARCH OF THE WORLD TRENDS OF MULTIMODAL TRANSPORTATION

2021 ◽  
Author(s):  
Svitlana Pron
Keyword(s):  
Transport System ◽  
Freight Transportation ◽  
Integrated Approach ◽  
World Market ◽  
Multimodal Transportation ◽  
Global Trends ◽  
Transport Services ◽  
Multimodal Transport ◽  
The World ◽  
The Moment

The role of multimodal transportation in the world market of transport services is defined in the article. Emphasis is placed on the significant pace of its development in a number of countries (first of all in the EU, the USA and China). It made it possible to minimize costs and increase interest to the countries in the international transportation system. It is established that development of multimodal (combined) transportation is considered to be promising in the growth of the transport system of Ukraine. International conventions and regulations of the world’s leading countries are analyzed. Typical features of multimodal transportation are singled out. Namely: implication of two or more modes of transport under one contract in the process of transportation; freight transportation under one document (through bill of lading); use of through tariff rate; presence of one of responsible parties – multimodal transport operator, that is responsible for freight from the moment of taking it under control up to the moment of its transfer to the consignee. Based on the study of the global trends in development of multimodal transportation, practices, which are of interest for further introduction in Ukraine, are defined. Namely: introduction of the effective transport policy in this field and creation of the regulatory base; development of multimodal transportation on the basis of the integrated approach, which provides for implementation of the relevant plans and programs; creation of multimodal transport system and new corridors; overcoming the infrastructural imbalance; construction of the modern multimodal logistic centers and their equipment; containerization of freight transportation; development of contrailer connections and expansion of the routes; improvement of services quality by introducing the latest innovative technologies for transportation process organization (use of electronic goods declaration; organization of digital transport corridors; electronic support of any chain of the freight delivery, use of the monitoring system for freight delivery control); implementation of the effective management system for risks arising in the process of multimodal transportation.

scholarly journals An overview of big data analysis

2020 ◽  
Vol 9 (4) ◽  
pp. 1646-1653
Author(s):  
Fabio Arena ◽  
Giovanni Pau
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Customer Service ◽  
Big Data Analytics ◽  
Short Review ◽  
Big Data Analysis ◽  
Digital World ◽  
Big Data Technologies ◽  
Definition Of ◽  
Smart Cars

Big data represents one of the most profound and most pervasive evolutions in the digital world. Examples of big data come from Internet of Things (IoT) devices, as well as smart cars, but also the use of social networks, industries, and so on. The sources of data are numerous and continuously increasing, and, therefore, what characterizes big data is not only the volume but also the complexity due to the heterogeneity of information that can be obtained. The fastest growth in spending on big data technologies is happening within banking, healthcare, insurance, securities and investment services, and telecommunications. Remarkably, three of those industries lie within the financial sector, which has many particularly serviceable use cases for big data analytics, such as fraud detection, risk management, and customer service optimization. In fact, the definition of big data analysis refers to the process that encompasses the gathering and analysis of big data to obtain useful information for the business. This paper focuses on delivering a short review concerning the current technologies, future perspectives, and the evaluation of some use cased associated with the analysis of big data.

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