Big Data Perspective Research Articles

The Transformation of Government Governance Model from the Perspective of Big Data

The information age has become the main feature of the development of the times. Promoting the modernization of the national governance system and governance capabilities is the overall goal of comprehensively deepening reforms. It is also the requirements and challenges that social and economic development and technological progress pose to the country and government in the new environment. This article uses literature research methods and logical deduction research methods to find that traditional government governance models have important characteristics such as the economicization of governance goals, the hierarchical governance of governance structures, and the proceduralization of governance methods. Therefore, it is necessary to use new technologies such as big data to realize the transformation and application of science and technology, the transformation and innovation of thinking, and the transformation of government governance mode.

Research on the application of Lean production mode in enterprises——Take Desi fogg company for example

Lean production is the third milestone of industrialization in human history. This is a revolutionary production and operation practice. Its core idea is to reduce waste, establish an efficient production system, improve corporate profits. It represents the direction in which manufacturing, or manufacturing standards, are moving today. With the continuous advancement of the information revolution, manufacturing enterprises must re-integrate external environmental information, transform their ideas and methods, and at the same time use big data and artificial intelligence to strengthen the manufacturing industry and form a new production mode. This article expounds the theory of lean production and basic methods from the perspective of big data and artificial intelligence. It combined with desi fogg existing organizational structure, site management, quality management, product development, production organization, problems existed in the aspects of applied research in the core of lean production ideas and principles, proposed the desi fogg lean production plan, and solve the management problems of the production process and waste of resources, establish the enterprise system of lean production system, and improve the level of the site management、 production quality and enterprise competitiveness.

Better Effectiveness of Multi‐Integrated Neural Networks: Take Stock Big Data as an Example

With the development of big data, in the financial market, the stock price prediction has many research directions from the perspective of big data. The classical time series prediction model cannot adapt to the high‐latitude information of stock data in the era of big data. The development of deep learning provides a new idea for high‐latitude stock data prediction. Four neural network models and three integrated learning models form different strategy sets, and the opening price of the next timestamp is predicted by backtracking information over the past 15 days with the characteristics of 12 indexes of the stock. The experimental results show that the prediction effect of the integration model based on the average weight policy and stacking policy is better than that of the single neural network, and the integration model based on stacking policy is expected to have the highest prediction accuracy and the minimum expected error. The accuracy was 80.2%, and the mean square error was 0.024. Compared with the single model, the accuracy is increased by 2%~7%, and the error is reduced by 0.01~0.03. The innovation of this article lies in the traditional machine learning thinking is applied to deep learning, as an individual with a variety of neural network to study, through the integration of learning strategies, fusion for the integration model, the experimental results show that the effect of the integrated model is better than that of a single model, to improve the robustness and accuracy of the model; the performance of the integrated model is more stable. For the utilization of big data resources, the integrated model of neural network has better prediction effect.

Analysis on Pricing Model of UBI Auto Insurance in China from the Perspective of Big Data

The auto insurance pricing model of UBI in china is a pricing model which offers special terms of premium to those drivers who have safer driving behaviour by evaluating and analyzing the behaviour of drivers and usage data of vehicles. According to document analysis method and comparative analysis method, this research compares the present UBI auto insurance pricing model in China and other countries. As a result, this study finds that pricing the vehicles by internet brings a increasing of premium income and profits, well, a decreasing of loss. Meanwhile, it is advantageous to market economy as it promotes the development of a industry chain, including car parts and services. However, pricing the vehicles by internet still depends on the developed model abroad due to the lack of domestic professionals and IT core system. Hence, comparing with traditional insurance, there is a significant advantage and a prospective future of UBI auto insurance pricing model. However, this UBI model faces challenges which requires it to justifies its development model continuously, matching up the pricing of drivers and vehicles preferably. As a consequence, the UBI model shall be more refinement in China.

Innovation Strategy of Enterprise Human Resource Management under the Background of Big Data

With the development of social economy, the era of big data has quietly arrived. Under this background, the demand for talents of enterprises will increase, and the management of human resources in enterprises will face huge amount of data, as well as more opportunities and challenges. From the perspective of big data, this paper introduces the meaning and characteristics of big data technology, briefly analyzes various problems existing in the traditional human resource management mode under the background of big data, and expounds how to innovate with the help of big data technology, make up for deficiencies in human resource management, explore the direction of human resource reform in the era of big data, seek a new mode of human resource management in the era of big data, and use big data technology to help enterprises develop and realize economic benefits.

Computer Network Security Maintenance and Management in the Era of Big Data

With the development of information technology, the big data network has been initially formed. However, the network security problem is becoming more and more serious in this environment. China has put the network security in an important position of national security. This paper analyzes the text of China's network security governance policy from the perspective of big data, builds an analysis framework based on big data, analyzes the relevant provisions of China's network security governance policy, analyzes the existing problems and puts forward optimization suggestions, in order to provide reference for the formulation of China's network security governance policy and improve China's network security governance policy system. This paper collects the information of network security incidents and policies in 2020 from Google data analysis platform. After preliminary collection and sorting, 195 relevant policies on network security governance are obtained. In order to ensure the accuracy and pertinence of the materials, all policies are analyzed. The research results show that the application of some big data of network security governance is concentrated on the regulatory level, lacking specific implementation rules and methods. There are 183 policies involving regulatory control, accounting for 31.6%. Therefore, the national network information department should cooperate with the legislative department to formulate policies for the problems in the network ecosystem. Network is not only the extension of people's daily life, but also an important part of economic and social life.

Guest Editorial: Scholarly Big Data

The papers in this special section focus on Big Data as it applies to scholarly information - or scholarly Big Data. This represents the vast quantity of research output, which can be acquired from digital libraries, such as journal articles, conference proceedings, theses, books, patents, experimental data, etc. It also encompasses various scholarly related data, such as author demography, academic social networks, and academic activities. The abundance of scholarly data enables the study of the academic society from a big data perspective. The dynamic and diverse nature of scholarly big data requires different data management techniques and advanced data analysis methods. Therefore, emerging topics such as scholarly big data acquisition, storage, management and processing are important issues for the research community.

Measuring the impact of COVID-19 on China’s population migration with mobile phone data

Population migration is an essential medium for the spread of epidemic, which can accelerate localized outbreaks of disease into widespread epidemic. Large-scale population movements between different areas increase the risk of cross-infection and bring great challenges to epidemic prevention and control. As COVID-19 can spread rapidly through human-to-human transmission, understanding its migration patterns is essential to modeling its spreading and evaluating the efficiency of mitigation policies applied to COVID-19. Using nationwide mobile phone data to track population flows throughout China at prefecture-level, we use the temporal network analysis to compare topological metrics of population mobility network during two consecutive months between before and after the outbreak, i.e. January 1st to February 29th. To detect the regions which are closely connected with population movements, we propose a Spatial-Louvain algorithm through adapting a gravity attenuation factor. Moreover, our proposed algorithm achieves an improvement of 14% in modularity compared with the Louvain algorithm. Additionally, we divide the period into four stages, i.e. normal time, Chunyun migration, epidemic interventions, and recovery time, to describe the patterns of mobility network’s evolution. Through the above methods, we explore the evolution pattern and spatial mechanism of the population mobility from the perspective of spatiotemporal big data and acquire some meaningful findings. Firstly, we find that after the lockdown of Wuhan and effective epidemic interventions, a substantial reduction in mobility lasted until mid-February. Secondly, based on the economic interaction and geographic location, China has formed an urban agglomeration structure with core cities centering and radiating toward the surroundings. Thirdly, in the extreme cases, the dominant factor of population mobility in remote areas is geographic location rather than economy. Fourthly, the urban agglomeration structure of cities is robust so that when the epidemic weakens or disappears, the city clusters can quickly recover into their original patterns.

Value Relevance of Accounts Receivable Factoring and Its Impact on Financing Strategy under the K-IFRS after COVID-19 from the Perspective of Accounting Big Data

This study investigates whether recognized accounts receivable (AR) factoring is more value relevant than disclosed AR factoring. After the adoption of the Korean International Financial Reporting Standards (K-IFRS), AR factoring is recognized as short-term debt, thus increasing firms’ leverage ratio. Using cross-sectional equity valuation regressions, we find that recognized AR factoring is value relevant, unlike disclosed AR factoring. Moreover, the market value of equity and AR factoring are more significantly correlated in highly leveraged firms than in less-leveraged ones. Accounting data are important from the perspective of big data. In the accounting industry as well, professionals started realizing the implications of big data. The COVID-19 pandemic has created a health crisis and wreaked havoc in an already-fragile global economy. Although there is no way to predict exactly what the economic damage from the COVID-19 pandemic will be, there must be widespread agreement that it will have severe financial impact on every company. Global financial markets have suffered dramatic falls due to the pandemic, and highly leveraged companies are in serious need of financing. While diving deeper, sound debt management and debt transparency are critical to ensure debt sustainability. Thus, companies would be willing to use AR factoring in order to overcome this financial status. This study also shows that highly leveraged firms decrease AR factoring after K-IFRS adoption.

Value of Mass Media in Food Safety Information Disclosure from the Perspective of Big Data

The advent of big data infrastructure has promoted the development of media forms and content. Food safety information disclosure (FSID) is an effective solution to regulate food safety issues. The mass media, government regulatory agencies, and food companies jointly participate in the disclosure of food safety information. Due to social responsibilities and common interests, a tripartite game relationship is formed. After an evolutionary game model was established with China as an example, the mass media’s participation in food safety information disclosure can affect the public’s decision-making, and true disclosure can promote the process and effectiveness; however, false disclosure will have adverse effects on all three parties. The application of big data technology doubles the positive and negative effects. Therefore, the government needs to strengthen the supervision of the mass media’s participation, and food companies need to actively provide correct disclosure information. The media should strengthen their management and use big data rationally, formulate corresponding disclosure strategies, and coordinate the three parties to promote food safety information disclosure.

Research on Innovation and Entrepreneurship Education for College Students from the Perspective of Big Data

Information technology promotes the wide application of big data and causes the constant reform of the teaching mode in colleges and universities. How to make college students adapt to the changeable market environment and improve the effect of innovation and entrepreneurship education has become a topic. Analyze the current situation of college students’ innovation and entrepreneurship education through big data. This paper analyzes college students’ professional ethics, job-hunting concept, innovation and practice ability cultivation, creative thinking, entrepreneurial psychological bearing ability, entrepreneurial core competitiveness, innovation and entrepreneurship comprehensive quality, etc. Through the analysis of the current situation of college students’ innovation and entrepreneurship education, we find out the positive influencing factors and consolidate and improve them. In view of the negative influence factors, this paper puts forward reform ideas and countermeasures. From the government, schools, teachers and students, the society summed up the innovation and entrepreneurship education model, to establish a set of constantly improved innovation and entrepreneurship curriculum system. In this way, the innovative and entrepreneurial talents training scheme adapted to the changing times can be formed and the healthy development of talents can be promoted.

Research on the impact of industrial agglomeration on industrial green efficiency from the perspective of big data

This paper uses the data of labor force, capital, all kinds of energy input, industrial pollutant emissions and output value of industries in 30 provinces and autonomous regions of China from 2011 to 2015, and uses big data and data envelopment analysis to calculate the industrial green efficiency of provinces and cities in China. At the same time, in order to explore the relationship between industrial agglomeration and industrial green efficiency, the panel regression model is used to conduct quantitative analysis. The results show that: whether from the national level or the eastern, central and western regions, industrial agglomeration can significantly promote the improvement of industrial green efficiency. Environmental regulation, industrial enterprise scale and other control variables can also significantly promote the improvement of industrial green efficiency in some areas, but not in some areas.

Detecting ransomware attacks using intelligent algorithms: recent development and next direction from deep learning and big data perspectives

Recently, cybercriminals have infiltrated different sectors of the human venture to launch ransomware attacks against information technology infrastructure. They demand ransom from individuals and industries, thereby inflicting significant loss of data. The use of intelligent algorithms for ransomware attack detection began to gain popularity in recent times and proved feasible. However, no comprehensive dedicated literature review on the applications of intelligent machine learning algorithms to detect ransomware attacks on information technology infrastructure. Unlike the previous reviews on ransomware attacks, this paper aims to conduct a comprehensive survey on the detection of ransomware attacks using intelligent machine learning algorithms. The study analysed literature from different perspectives focusing on intelligent algorithms detection of ransomware. The survey shows that there is a growing interest in recent times (2016—date) on the application of intelligent algorithms for ransomware detection. Deep learning algorithms are gaining tremendous attention because of their ability to handle large scale datasets, prominence in the research community, and ability to solve problems better than the conventional intelligent algorithms. To date, the potentials of big data analytics are yet to be fully exploited for the smart detection of ransomware attacks. Future research opportunities from the perspective of deep learning and big data analytics to solve the challenges identified from the survey are outlined to give the research community a new direction in dealing with ransomware attacks.

Analysis of Art Criticism from the Perspective of Big Data from the Perspective of All Media Communication of Film and Television Art

As a different art evaluation activity, art criticism has a very positive and powerful influence on the creators of art. With the advent of big data technology, the types of art criticism of film and television art from the perspective of all media are also changing correspondingly. The fragmentation of criticism form, the subjectivity of criticism content and the diversification of criticism subject have been discovered. However, the characteristics of these criticisms not only promote the prosperity of art criticism from the perspective of big data, but also bring a lot of negative effects to people. This paper briefly summarizes the concept of art criticism and the main characteristics of all media art criticism from the perspective of computer big data, and finally draws the corresponding conclusions.

Cognitive Analytics for Rapid Stress Relief in Humans Using EEG Based Analysis of Tratak Sadhana (Meditation)

The article reports the effect of Tratak Sadhana (meditation) on humans using electroencephalograph (EEG) signals. EEG represents the brain activities in the form of electrical signals. Due to non-stationary nature of the EEG signals, nonlinear parameters like approximate entropy, wavelet entropy and Higuchi' fractal dimensions are used to assess the variations in EEG rest as well as during Tratak Sadhana, i.e. at a rest state with eyes closed and during Tratak meditation. EEG signals are captured using EPOC Emotive EEG sensor. The sensor has 14 electrodes covering human scalp. Results shows that new practitioners can also achieve a rapid meditative state as compared to other meditation techniques. Further, the Big Data perspective of the present study is discussed. The present study shows that Tratak Sadhana meditation is an effective tool for rapid stress relief in humans.

An empirical investigation of online review helpfulness: A big data perspective

This study investigates the determinants of online review helpfulness, adopting various predictors from three dimensions of the online review management: source factors, review factors, and context factors. Based on a large, comprehensive dataset that includes 14,051,211 online reviews in 24 product categories from an ecommerce retailer, Amazon.com, this study provides empirical evidence on the effect of source and reviews factors on perceived review helpfulness, which the extant literature has reached inconsistent conclusions. In addition, this study considers the effects on review helpfulness created by context factors: product satisfaction, product popularity, product intangibility, and product variety. These factors are scarcely discussed in the existing literature. The major findings include (1) review extremity, review depth, and reviewer expertise have a positive effect on review helpfulness; (2) review inconsistency, product intangibility, product satisfaction, product popularity, product variety, and reviewer experience have negative effects; and (3) product intangibility moderates the effect of review extremity and depth on review helpfulness.

Network Communication Based on Embedded Microprocessor Accelerates the Development of Preschool Education

<p style="text-align: justify;"><span style="font-family: "times new roman";">With the advent of the information age, all kinds of data and information emerge in endlessly, and the various network information data that students are exposed to are more and more complicated. How to strengthen the ideological and political education of college students in the new era of network information to ensure that students can It is a new challenge faced by every college educator to have good ideological and ethical qualities and to grow up healthily in the rapidly changing network environment, so as to realize the all-round development of students. Therefore, in response to the current difficulties and challenges of online ideological and political education, colleges and universities should adopt targeted strategies in a timely manner to continuously strengthen college students' awareness of online information security to meet the development requirements of the era of big data. This article will discuss the real dilemma and countermeasures of college network ideological and political education from the perspective of big data, hoping to provide valuable reference and reference for related research.</span>

Research Progress of Network Public Opinion Based on Fuzzy Set from the Perspective of Big Data

Granular computing is a new method of intelligent information processing. It describes knowledge and simulates human thinking mode in a granular way. It can realize the rapid transformation between different granular knowledge and is suitable for the analysis and modeling of large-scale and complex data. Fuzzy set is an important model in granular computing, which describes uncertain problems through fuzzy membership function and has been widely used in many fields such as network public opinion analysis. This paper combs the application research of fuzzy sets in network public opinion analysis from the perspective of big data, and discusses the different roles of fuzzy sets in network public opinion analysis from four aspects: fuzzy comprehensive evaluation, fuzzy reasoning, fuzzy granularity and generalized fuzzy set. The characteristics of different methods are compared and the unique features of fuzzy sets in these four aspects are pointed out. The unique advantages and some problems are discussed as well, and the future trend of this field is discussed.

Signal Processing from the First Exploration of Big Data Horizon

&lt;p&gt;Under the background of the popularization and application of computers in various fields, the amount of diversified data has been greatly increased, which also means that the era of big data is gradually coming. At the same time, various signal equipment and sensor equipment are widely used in various fields. People from the perspective of big data expect to collect, analyze and process the signal data by means of big data. However, due to the diversity of signal data and the huge and complex amount of data, the future development trend of signal from the perspective of big data is the information fusion of signal data. Therefore, it is necessary to explore signal processing from the perspective of big data. With the application of signal processing technology, the efficiency and quality of information processing can be improved gradually.&lt;/p&gt;

Enlightenment on Vocal Music Classroom Teaching from the Perspective of Big Data

With the arrival of the era of big data, mobile Internet, Internet of things, cloud computing, etc. are constantly changing our lives. The high-speed development of information technology is boosting the development of educational information, restructuring our classroom teaching and learning methods, which of course also includes vocal classroom teaching. This paper briefly introduces the relationship between the era of big data and vocal music classroom teaching, analyzes the factors affecting vocal music classroom teaching from the big data, and studies the new ideas of vocal music classroom teaching from the application of information technology. At the same time, this study also describes the role of information technology-based “singing situation” teaching in vocal music teaching, such as skill training, voice image shaping, blending scenes, etc., to guide students to apply the emotional characteristics of singing thinking to singing art activities, and strive to provide some new enlightenment for the current vocal music classroom teaching through the power of scientific and technological information. It also provides theoretical basis for the innovation and reform of vocal music classroom teaching.