New Engine to Promote Big Data Industry Upgrade

Based on theoretical analysis, we select the relevant data of 30 provinces (autonomous regions and municipalities) in China from 2013 to 2019, and empirically test the impact of financial technology on the development of big data industry and its mechanism using dynamic panel data model, mediating effect test method and threshold effect model. The benchmark regression results show that the regression coefficient of financial technology to big data industry is significantly positive at the significance level of 10%, indicating that the financial technology can directly promote the development of big data industry. The regression coefficient of the dynamic lag term of big data industry is negative, but not significant, indicating that the dynamic lag effect of big data industry is not obvious. The mediating effect test results show that the financial technology can indirectly promote the development of big data industry by alleviating the big data enterprise financing constraints. The big data enterprise financing constraints have a partial mediating effect, and the mediating effect account for 27.63% of the total effect. In addition, the threshold effect test results show that the direct effect of financial technology on big data industry is significantly enhanced when the development level of financial technology is higher than 5.8790, that is, there is a positive threshold effect of financial technology directly promoting the development of big data industry. However, the indirect effect of financial technology on big data industry is relatively weak when the development level of financial technology is higher than 5.4328, that is, financial technology indirectly promotes the development of big data industry by alleviating the big data enterprise financing constraints, which has a negative threshold effect.

The primary purpose of this book chapter is to investigate the applications and benefits of big data analytics in fashion retailing to discover the opportunities created by big data in this industry. Fashion has a substantial share of data production, and big data in the fashion industry has created countless challenges and opportunities. Today, the analysis of data obtained from various sources as a critical factor to smart fashion retailing can lead to strategies for better decision-making under conditions and environments of uncertainty. Machine learning algorithms can accelerate the analysis of big data by finding patterns and uncovering trends. By categorizing and recognizing patterns, it can turn incoming data into insights useful for business process operations. This study is based on various secondary sources, which are inherently descriptive and qualitative. This section of the book examines the potentials of big data and machine learning in the fashion retail industry. In the end, it discusses possible ways that big data can be applied in fashion retailing in order to gain insights and identify customer behavior patterns through big data analytics and AI applications and the ethics-related issues in retailing.

The purpose of this research is to analyze big data structure and various objects in big data industry based on ecological perspective. Big data is rapidly emerging as a highly valuable resource to secure competitiveness of enterprise and government. Accordingly, the main issues in big data are to find ways of creating economic value and solving various problems. However big data is not systematically organized, and hard to utilize as it constantly expands to related industry such as telecommunications, finance and manufacturing. Under this circumstance, it is crucial to understand range of big data industry and to which stakeholders are related. The ecological approach is useful to understand comprehensive industry structure. Therefore this study aims at confirming big data structure and finding issues from interaction among objects. Results of this study show main framework of big data ecosystem including relationship among object elements composing of the ecosystem. This study has significance as an initial study on big data ecosystem. The results of the study can be useful guidelines to the government for making systemized big data ecosystem and the entrepreneur who is considering launching big data business.

Technologies related to the Big Data term are increasingly focusing the industrial sector. The underlying concepts are suited to introduce disruptive changes in the various ways information is generated, integrated and used for optimization in modern production plants. Nevertheless, the adoption of these web-inspired technologies in an industrial environment is connected to multiple challenges, as the manufacturing industry has to cope with specific requirements and prerequisites that differ from common Big Data applications. Existing architectural approaches appear to be either partially incomplete or only address individual aspects of the challenges arising from industrial big data. This paper has the goal to thoroughly review existing approaches for industrial big data in manufacturing and to derive a consolidated architecture that is able to deal with all major problems of the industrial big data integration and deployment chain. Appropriate technologies to realize the presented approach are accordingly pointed out.

Industrial big data is an important part of big data family, which has important application value for industrial production scheduling, risk perception, state identification, safety monitoring and quality control, etc. Due to the particularity of the industrial field, some concepts in the existing big data research field are unable to reflect accurately the characteristics of industrial big data, such as what is industrial big data, how to measure industrial big data, how to apply industrial big data, and so on. In order to overcome the limitation that the existing definition of big data is not suitable for industrial big data, this paper intuitively proposes the concept of big data cloud and the 3M (Multi-source, Multi-dimension, Multi-span in time) definition of cloud-based big data. Based on big data cloud and 3M definition, three typical paradigms of industrial big data applications are built, including the fusion calculation paradigm, the model correction paradigm and the information compensation paradigm. These results are helpful for grasping systematically the methods and approaches of industrial big data applications.

Using Big Data in Industrial Milk Powder Process Systems

Big Data technology is used to store, convert, transmit and analyze large quantities of dynamic, diversified data, which may be structured or unstructured data, for the purpose of commercial or social benefit. Big data technology applications need to be able to undertake real-time, high-complexity analysis of vast amounts of data, to help business enterprises perform decision-making within the shortest possible timeframe. With the rapid pace of development in cloud computing applications, both public cloud and private cloud data centers are continuing to accumulate enormous volumes of data. As a result, big data technology applications are becoming ever more important. This paper will analyze recent development trends in the field of big data industry for the reference of interested parties.

This study employs fuzzy set qualitative comparative analysis to explore factors influencing the development of the big data industry in 31 provinces and municipalities in China. By analyzing eight representative variables across four dimensions—new infrastructure construction, data resources and applications, government services, and resource input—the study aims to uncover configurations and pathways that affect industry growth. The results reveal four effective strategies: combining infrastructure with data application, integrating government services with data application, coupling infrastructure with resource investment, and focusing solely on government services. Core variables such as optical cable length, policy environment, and service environment require governmental intervention to foster development. Therefore, continuous enhancement of new infrastructure construction, supporting the development of small and medium-sized Enterprises in big data, strengthening the big data industry and government service environment, and increasing investments in talent and capital are recommended to drive industry progression.

The year of 2011 is considered the first year of big data market in China. Compared with the global scale, China's big data growth will be faster than the global average growth rate, and China will usher in the rapid expansion of big data market in the next few years. This paper presents the overall big data development in China in terms of market scale and development stages, enterprise development in the industry chain, the technology standards, and industrial applications. The paper points out the issues and challenges facing big data development in China and proposes to make polices and create support approaches for big data transactions and personal privacy protection.

The sudden outbreak of COVID-19 has greatly affected the development of all industries, and the development of many enterprises has been severely impacted. In the context of epidemic prevention and control, the Internet has brought new development space for enterprise marketing, so more and more enterprises begin to enter the field of online marketing. With the continuous progress of Internet technology and the deepening of informatization, China's big data industry has made qualitative progress. The traditional marketing model cannot meet the needs of the increasingly fierce market competition. By applying big data technology to network marketing, enterprises can dig deeply into user information and formulate corresponding marketing strategies based on users' preferences, behavior patterns and shopping habits, so as to realize precise marketing and improve their economic benefits by mining potential customers. However, there are also some problems in the process of using big data technology to move towards precision, such as serious homogenization, low application level, and privacy security issues. Only by solving these problems can enterprises use big data to achieve higher quality development.

Today, all Internet technologies are being constructed on the basis of big data, the so-called Internet of Everything, which is said to be a comprehensive service for individuals, facilitating peoples life, enriching peoples brain, and improving the quality of life and work efficiency. On the other hand, Restrictive laws and regulations have not been developed and improved along with big data, but rather lag behind. This has resulted in loopholes in the big data industry, giving all the bad people the opportunity to take advantage of personal data reselling, privacy disclosure, and even the use of big data to influence personal habits, such as daily work and rest, shopping and consumption, education and cultural beliefs. This paper lists the benefits of big data to various groups, as well as the hidden problems in the development of this technology. Through literature analysis, this paper finds that big data is a great threat to the privacy and security of the masses. At the end of the paper, some constructive suggestions are put forward to protect public privacy.

The transmission features of the COVID-19 epidemic have still not been properly recognized in the sense of global environmental changes. Likewise, the pace of global development, increased population size, nuanced encounters, and lack of medical security in developing countries all contribute to the complexities of COVID-19 prevention and regulation. The COVID-19 seems to have a greater impact on the global economy when compared to severe acute respiratory syndrome (SARS) that occurs in 2013. The bone of contention is how to fight the spread of these infectious diseases and how to contains it spread from human-to-human. Most countries depended on the observation of classic steps to monitor diseases and public health to contain the COVID-19 pandemic, similar to those used with SARS in 2003. Data in the healthcare sector are usually huge and not easy to handle, this results from the enormous way by which data grows in the healthcare sector, the rate at which data are been produced, and the variety of various data in the healthcare system contributed to this growth. Also, the rate at which these data are been captured, stored, analyzed, and retrieved in healthcare has swiftly from the aged paper-based storage technique to the use of digital techniques and methods. Hence, the application of advanced technologies which includes virtualization and cloud computing allows for huge and effective data processing in the healthcare system. Thereby rapidly turning the healthcare system into a big data industry. Nevertheless, in these modern days, the improvement in the information and communication technologies (ICT) brings the advancement of varied data from new sources in the healthcare system. Therefore, this chapter discusses the possible application of big data and analytics to improve conventional public health approaches to tackle the COVID-19 pandemic to control, manage, identify and avoid COVID-19 and reduce the effect on health implicitly associated with COVID-19. The significant applications of big data and challenges are presented. Leveraging big data and intelligent analytics becomes expedient in other to enjoy their use for COVID-19 disease outbreak and public health.

Taking Artemisia industry, the pillar industry of Qichun County as an example, this paper studies the role of big data in the construction of regional industrial supply chain, and uses big data technology to analyze the application of modern information technology in the industry, such as the system of county industrial supply chain, product structure and e-commerce. The main problems of the industrial supply chain in Qichun County are: insufficient application of big data technology in the whole supply chain, low application of big data technology in the traditional manufacturing industry, low application of big data in the tourism and health industry, and low intelligence of the logistics system. It is proposed to use big data and other information technology to optimise the supply chain upgrade of the county industry and establish a complete industrial chain of smart manufacturing, smart logistics, smart tourism and artificial intelligence + big health.

The tremendous growth in technology is also the cause of global warming, which is harmful greenhouse gas Emissions. The information and communication technology (ICT) sector is one of the fastest-growing industries; it has the most significant impact on almost all other technologies. Energy efficiency and reduction of global warming is now a wish and realization by all key players related to this technology. The big data is one of the modern tools of ICT. It not only has a range of energy efficiency, but it can also help other departments to become intelligent energy efficient. This research article proposes several ways to achieve the mentioned goal, like optimization of battery size, overcoming uncertainties in energy generation, and overcoming storage and consumption. In a green data warehouse, the energy supply from renewable energy sources and the user traffic load are dynamic. The big data industry is also aware of the potential benefits of renewable energy to make the future more environmentally friendly and sustainable. The research progress of big data storage solutions is reviewed in this paper.

4차 산업혁명 시대에 사물인터넷과 모바일을 통해 수집된 다양한 정보를 이용해 공공 및 민간영역의 새로운 비즈니스모델을 위한 빅데이터 산업이 각광을 받고 있다. 하지만, 개인정보 비식별화 조치를 통한 빅데이터 통합 및 분석를 수행하면서 개인프 라이버시가 노출될 위험성을 여전히 가지고 있다. 최근 개인정보를 노출하지 않고 데이터의 가치를 유지하는 방법에 대한 연구가 진행되고 있다. 본 논문에서는 빅데이터산업 활성화를 위해 의료, 농업 등 산업별로 개인정보 보호체계가 필요함을 강조하였다. 비식별화된 개인정보의 적정성 평가 기준을 개인 민감정보 중심의 의료분야는 k-익명성 최소값을 일반적인 산업분야의 평균값 보다 높은 5 이상으로 설정해야하며, 농업분야에서는 개인별 민감정보범위에 개인소유 반려견이나 농지 정보를 포함시켜서, 산업별 특성에 맞게 개인정보 보호체계를 보완해야하며, 해당 산업의 특정지역을 대상으로 먼저 실증을 거처 전국적으로 확산하는 것을 제안한다.In the era of the 4th industrial revolution, the big data industry is gathering attention for new business models in the public and private sectors by utilizing various information collected through the internet and mobile. However, although the big data integration and analysis are performed with de-identification techniques, there is still a risk that personal privacy can be exposed. Recently, there are many studies to invent effective methods to maintain the value of data without disclosing personal information. In this paper, a personal information protection system is investigated to boost big data utilization in industrial sectors, such as healthcare and agriculture. The criteria for evaluating the de-identification adequacy of personal information and the protection scope of personal information should be differently applied for each industry. In the field of personal sensitive information-oriented healthcare sector, the minimum value of k-anonymity should be set to 5 or more, which is the average value of other industrial sectors. In agricultural sector, it suggests the inclusion of companion dogs or farmland information as sensitive information. Also, it is desirable to apply the demonstration steps to each region-specific industry.