Forecast of Development Trends in Big Data Industry

In 2020, the development of the big data industry will face severe challenges of transformation and upgrading. This article explores a series of issues in the big data industry upgrade from two levels: big data industrialization and big data industry chain. For the industrialization of big data, the industry starts from the core construction of big data industrialization, utilizes general big data platform as framework, and aggregates the horizontal industrial ecological layout and the vertical industrial ecological chain to form horizontal and vertical big data platforms respectively. The industry chain for big data is equipped with a docking mechanism that is different from the traditional chain association relationship. Through the multi-dimensional support and expansion of the big data industry chain, the framework and map of the industry chain are constructed, and the generation of a variety of technical models, system and methods of the big data industry chain is promoted. Finally, a big data product system composed of hardware products, software products, and integration products of both is built. The above analysis will help improve the service performance of the big data industry, improve the data connectivity of technology and the environment, and ultimately promote the big data industry upgrade.KeywordsBig dataBig data industrializationBig data industrial chainIndustrial 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.

With the development of the times, the progress of society, the continuous improvement of human science and technology and cultural level, big data technology has been widely used in people’s daily production and life. Under the background of the current big data era, China’s economic system reform of all walks of life is in full swing, and the economic system and structure of all walks of life are gradually improving. The development trend of business management industry is also the same. In today’s business management industry, the working mode and working mode have changed greatly compared with the past. In order to cope with this change, this paper introduces big data technology into the hot issues analysis and development trend research of business management, so as to better analyze and study the development problems of business management. There are many hot issues in the business management industry, which need constant attention from the industry and related fields. The introduction of big data technology can greatly reduce the work intensity of the staff and enhance the work efficiency. Through long-term research, it can be found that some analysis methods proposed in this paper can be effectively used to help the staff engaged in business administration to analyze the importance of some hot issues in business administration and predict the importance of the development trend.

In order to conduct research and analysis on the construction of application-oriented undergraduate data science and big data technology courses, the professional development characteristics of universities and enterprises should be taken into consideration, the development trend of the big data industry should be scrutinized, and professional application-oriented talents should be cultivated in line with job requirements. This paper expounds the demand for capacity-building professional development in application-oriented undergraduate data science and big data technology courses, conducts research and analysis on the current situation of professional development, and puts forward strategies in hope to provide reference for capacity-building professional development.

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.

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.

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.

With the rapid development of information technology, big data has become an important force for social progress. Under such a background, the education carried out for big data majors in major universities across the country has become more and more extensive, and in order to improve the quality of education and teaching, the integration of industry and education in big data majors is very necessary, especially the higher vocational colleges and universities, as an important base for cultivating high-end technological talents, are paying more attention to the major of big data technology and application. Therefore, how to effectively train talents so that they can meet the needs of the development of big data industry has become an urgent problem. As an effective talent cultivation mode, the integration of industry and education has shown great potential in the big data technology major of higher vocational colleges and universities. Based on this, this paper will explore and study the talent cultivation of industry-teaching fusion in big data technology majors in higher vocational colleges, with a view to providing reference and guidance for the later development of the work.

Using Big Data in Industrial Milk Powder Process Systems

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.

The refinement and rationalization of urban design has become an important trend of restructuring and development during the crucial stage of China's urban transformation. At the same time, big data technology is developing rapidly, with the availability of various types of high-precision data being gradually increased. The combination of big data and urban design plays an important role in the urban design of transition period. This paper describes the design of urban education system under the big data platform. With the traditional teaching model, which include four stage (research, topic, design and expression), big data technologies are divided into four aspects to consider how to apply the big data to the teaching of urban design. Specifically summarized: big data accelerate researching, big data secondary analysis, big data enhanced design and big data of optimization expression. The aim of this research is to let the traditional teaching system and cutting-edge technology of large data collide with each other fusion, which can not only enrich the existing education system, but also establish a complete technical structure. On this basis, it can help students establish and improve the construction, and the ability to learn new technologies, to promote the progress of urban design techniques.

The volume of data in the world is growing very fast and generated from verity of sources like social media, sensors airline industry or scientific data in different formats. Biggest challenge is how to infer meaningful insights from such a varietyful and big data along with concern of data storage and management of fast growing data. The size of the databases used in today’s enterprises has been growing at exponential rates day by day. Hence, industries requirement to quickly process and analyze the big data volumes for business decision making and customer insights has also grown exponentially. Data pouring from various sources may be can be structured or unstructured in nature. Structured data refers to a relatively well-organized information, which can be further inserted into traditional RDBMS. As Traditional RDBMS are efficient and easy queries by simple, straightforward search algorithms or SQL queries. In contrast to structured data, unstructured data can be considered as information, which does not, comes in a pre-defined data format, well organized data storage model, or cannot be stored well into relational tables. It is assumed to be fastest growing type of data, e.g. image, sensors data, web chats, social networking messaging data, video, documents, log files, and email data. There are many techniques and software available, which can process and provide efficient storage of unstructured data and help organization to perform analytics on unstructured data. Unstructured data does not well-organized and not stored in predefined manner e.g. logs, web chats. The variety and on ordered nature of data makes storage methods and structure makes execution a time and resource-consuming affair. Advancement into technology has open floodgates to push huge volume of unstructured type of data. Multimedia data is one of the example of unstructured big data, which spans all over the Internet. This needs high execution capability to extract useful information. Rapid processing of multimedia data such as video is important for e.g. criminal investigations, surveillance monitoring, news analysis, sports analytics domain, emotion extraction, etc. Hence, analysis of multimedia data in minimum timeframe is one of the latest research areas. Therefore, we have researched techniques for analyzing unstructured data to extract meaningful information hidden in the big data. In addition, we will describe about various techniques and software used to Manage, process unstructured big data in efficient manner, and increases the performance of complexity analysis.

Cloud computing and big data: Technologies and applications

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.

With the advent of the big data era, the amount of various types of data is growing exponentially. Technologies such as big data, cloud computing, and artificial intelligence have achieved unprecedented development speed, and countries, regions, and multiple fields have included big data technology in their key development strategies. Big data technology has been widely applied in various aspects of society and has achieved significant results. Using data to speak, analyze, manage, make decisions, and innovate has become the development direction of various fields in society. Taxation is the main form of China’s fiscal revenue, playing an important role in improving the national economic structure and regulating income distribution, and is the fundamental guarantee for promoting social development. Re examining the tax administration of tax authorities in the context of big data can achieve efficient and reasonable application of big data technology in tax administration, and better serve tax administration. Big data technology has the characteristics of scale, diversity, and speed. The effect of tax big data on tax collection and management is becoming increasingly prominent, gradually forming a new tax collection and management system driven by tax big data. The key research content of this article is how to organically combine big data technology with tax management, how to fully leverage the advantages of big data, and how to solve the problems of insufficient application of big data technology, lack of data security guarantee, and shortage of big data application talents in tax authorities when applying big data to tax management.