Application Of Big Data Technology Research Articles

Caching or not: An online cost optimization algorithm for geodistributed data analysis in cloud environments

With the wide application of big data technology, a large number of data geographically stored in data centers across various regions are generated everyday, waiting to be analyzed by big data tasks. Examples of such data analysis tasks include weather prediction and intelligent healthcare applications. Clouds are being used by more and more enterprises due to their nearly infinite resources, ease of scaling, and other characteristics. Organizations often rent StaaS (Storage-as-a-Service) storage products offered by cloud providers, such as OSS (Object Storage Service), for massive data storage, while building a big data cluster in cloud environments to analyze the collected data from various regions. However, when the data to be analyzed are not located in the rented cluster, how to efficiently and economically process the distributed input data stored in clouds becomes an urgent problem to be solved. A simple approach is to only cache frequently accessed data rather than all data from other regions into the cluster to reduce total traffic costs. However, it is generally very hard to predict future data access curves. Thus, a rash caching decision may incur more costs. To address this problem, in this paper we propose an online algorithm for guiding cloud users to make cost-effective caching decisions properly, while not requiring any future information. We prove theoretically that the competitive ratio of our online algorithm is less than 2. Finally we verify the effectiveness of our proposed algorithm through extensive experiments based on the real price of Alibaba’s public IaaS cloud products using both real-world Yahoo S2 data and synthesized datasets.

Use of Big Data for Textile Fashion and Garment Industry

Big data is such type of data which requires a different processing approach. The feature of data science is to drive the fashion and textile industry forward. Among the revolutionary technologies big data is the most useful tool for industries. One of the most basic questions in research is how block chain empowered by big data can be handled and utilized to manage traceable and information sharing more perfect through global supply chains. At present the number of applications of big data technology in textile manufacturing sections in organized manner is limited. In fashion apparel businesses there are numerous benefits of retail analytics. Data related to style designs is the information about the components and standards of plan, which joined together, give the plan of a design item. Big data can allow fashion industry to examine popular patterns at a granular level to allow them understand about who is buying and why.

Digital Marketing Strategy, Brand Awareness, Application of Big Data Technology, Impact on Business Success

Digital Marketing Strategy, Brand Awareness, Application of Big Data Technology, Impact on Business Success

Application and Effectiveness Evaluation of Business Data Analytics in Marketing Strategy

The application of data analytics is becoming increasingly important in the field of marketing. This chapter explores in detail the application of data analytics in marketing strategy, including market segmentation, consumer behavior analysis, competitive analysis, and market trend forecasting. Through descriptive, predictive, and prescriptive analyses, companies are able to gain a deeper understanding of the current state of the market, predict future trends, and formulate a course of action accordingly. The chapter also discusses various data analytics tools and software, as well as the application of big data technologies in marketing, emphasizing the importance of data privacy and security. Finally, the chapter emphasizes the importance of evaluating the effectiveness of marketing campaigns, including ROI and long-term effectiveness evaluation, as well as how to combine quantitative and qualitative methods for a comprehensive evaluation. Through these methods, organizations can allocate resources more efficiently and improve the efficiency and effectiveness of marketing campaigns.

Research on the Current Application of Big Data in Public Governance

Computer technology has profoundly impacted contemporary society, and the social governance of the digital era is inseparable from big data technology. The importance of data statistics in the field of public governance is increasingly prominent, as it can aid government decision-making, improve the quality and efficiency of services. By deeply analyzing data, precisely meeting public demand, increasing service speed and execution, and through real-time monitoring of social data, the government can grasp social dynamics, pre-warn risks, formulate targeted policies, and ensure social stability and harmony. At the same time, attention is paid to the problems that may arise from the application of big data technology, fully leveraging the unique advantages of electronic information technology, establishing and improving relevant laws and regulations to ensure its healthy and orderly development.

Optimization of teaching methods and allocation of learning resources under the background of big data

In the context of the wide application of big data technology, it is particularly important to optimize the allocation of teaching methods and learning resources. This study first expounds the key role of big data in the optimization of teaching methods and the allocation of learning resources, and emphasizes how big data technology promotes the transformation and development of education and teaching models. Based on the analysis of traditional models of teaching method optimization and learning resource allocation, this study proposes a new model driven by big data. By accurately identifying students’ learning needs and behavior patterns, the model optimizes teaching methods and allocation of learning resources. This study introduces the whole process of data collection, cleaning, analysis and modeling. In the process, it shows how big data can be integrated, analyzed, and applied to further support the construction and validation of models. Through empirical research and effect evaluation, this study proves the validity of the model of teaching method optimization and learning resource allocation driven by big data, and demonstrates how big data can promote educational equity and improve educational quality.

Empowerment of Precise Ideological and Political Education in Higher Education with Educational Digitalization

The study explores how educational digitalization enables the precise development of ideological and political education in colleges and universities. Digital transformation enables colleges and universities to accurately define educational objectives, content strategies, effect evaluation, and process management, and realize the precision and intelligence of ideological and political education. The application of big data technology enhances the data-oriented thinking of teachers and students, promotes the accurate application of data, and improves the efficiency of ideological and political education. The research also prospected a new vision of the digital construction of ideological and political courses and clarified the theoretical and practical path of the implementation and evaluation mode of ideological and political courses under digital empowerment. Education digitalization enables precise ideological and political education, which is a key way to promote the innovative development of ideological and political education in colleges and universities and will strongly support the improvement of the overall quality of higher education and the training of excellent talents.

Research on the Application of Big Data Technology in Extracurricular Sports for Teenagers

Research on the Application of Big Data Technology in Extracurricular Sports for Teenagers

Research and Practice of Online Postgraduate Quality Management Systems in Local Institutions with the Context of Digital Network

In the digital era, there is an urgent need to reconstruct the talent cultivation system with the support of computer technology, and it is crucial to create and build a quality control system for postgraduate education that meets the development of the times. The system is usually operated online using digital network technology. Most of the postgraduates admitted to the research group come from the same level of institutions or non-graduate training authorization units, and their scientific research ability is relatively weak, hence they need more guidance and help from the university and supervisors in the cultivation process. Relying on the university's "Intelligence +" academic characteristics, the group has explored and practiced digital transformation in terms of accurate publicity, quality improvement, and differentiation cultivation, which mainly includes the following two aspects. The first, take the online teaching management platform as the core, cooperate with the enrollment platform, the practice results platform, and the graduation work platform through the network, with the user experience of teachers and students as the test standard, mine data on the habits of teachers and students, realize the orderly operation of the data-driven postgraduate education process. The second is to improve the teaching management ability and innovation consciousness of teachers under the intelligence education conditions through data platform and online or underline staged training. At the same time, the Group will continue to pay attention to data security and privacy protection issues in the postgraduate QMS, and deeply explore the integration and application of big data technology and education management, so as to improve the management level of postgraduate education and adapt to the new situation and requirements of postgraduate education.

Database Access Information Security Management Simulation under Big Data Platform

When people perform database access information security management, the traditional method cannot accurately verify the identity of the visitor, the credibility of the identity information, and the security management of the access information. With the widespread application of big data technology, the amount of data in databases is rapidly increasing, which brings new challenges to information security management. The main purpose of this study is to explore how to more effectively manage the security of database access information on big data platforms. Therefore, the trusted computing platform is established to implement database access information security management under the data platform. The method determines the user behavior is credible by establishing a behavior chain of behavior based on the user identity and measuring user operation behavior. For the user's private data, the encryption/decryption module is used for security protection, preventing data from being leaked through illegal copying. A trusted metric model based on the USB Key user identity is established and a trusted platform is established. By improving the ELGamal algorithm, the IMC/IMV metrics architecture is utilized to measure platform security attributes. In the first round of anonymous authentication, the identity authentication of the platform is completely completed, and the database access information security management under the big data platform is completed. The simulation results show that in 10 experiments, the transmission time delay of TCP/IP protocol is less than 200ms, and the security of database access information is enhanced after the encryption system is established in the database. This has certain theoretical enlightenment for the improvement of database security and the optimization of information security management.

How Does the Government Facilitate the Co-Production of Digital Public Safety Services?—Based on Empirical Evidence from Shenzhen

The application of big data technology provides support for the co-production of public safety services. Existing research often focuses on how technology influences co-production, but lacks attention to the key actors that drive co-production and the mechanisms that facilitate it. This study examines the role of government in the digital co-production of public safety services, using the practice of Shenzhen as a case study. Shenzhen has built 125 information systems based on over 100 billion pieces of big data, forming a model of digital safety service co-production. The study reveals three types of digital co-production, characterized by “government-business joint planning, passive participation of businesses and the public, and active cooperation among government, businesses, and the public” in the “design-production-application” stages. The study shows that the government is not only a co-producer but also a proactive actor in activating the willingness of non-governmental entities to participate. Local governments mobilize non-governmental participation through three mechanisms: empowerment, profit enhancement, and value co-creation. The “power-interest-value” paradigm is applicable for analyzing the co-production of public services and helps to explain the transformation mechanisms of co-production behavior.

Challenges and Countermeasures of Youth Moral Education in the Era of Big Data

Youth moral education is an extremely important work, which is related to the inheritance of traditional virtues of the Chinese nation, the fundamental plan of “successor,” and the construction of modern Chinese spiritual civilization. In the era of big data, the moral education of youth is facing new challenges and opportunities. The wide application of big data technology has brought about an information explosion, exposing young people to more diverse and complex social information. It not only brings opportunities for positive moral education but also many challenges. The main problems of youth moral education in the era of big data are neglected youth subjectivity, insufficient youth moral practice, and utilitarian youth value orientation. We should develop personalized moral education programs, strengthen the integration of moral knowledge and action, and pay more attention to moral practice to improve the pertinence and effectiveness of moral education for young people in the era of big data.

Exploring Big Data Technology in Human Resources Decision-Making

With the widespread application of big data technology, human resources decision-making faces new opportunities and challenges. This paper explores the application of big data technology in human resources management and its potential impact, providing an in-depth analysis of the transformation of the role of HR managers in the era of big data. Firstly, the application of big data enables HR professionals to make more effective strategic business decisions by eliminating biases in decision-making and providing profound real-time insights. This, in turn, attracts and retains talented individuals, enhancing business efficiency. Secondly, big data plays a crucial role in the recruitment process, assisting management in avoiding recruitment errors. Personalized data analysis helps identify talents that match specific job requirements, ultimately improving long-term performance. Furthermore, the application of big data technology facilitates organized internal mobility of human resources within a unit. By analyzing employee data, organizations can gain better insights into employees' work capabilities and development potential, offering diverse opportunities for career development to enhance employee satisfaction, loyalty, and maintain competitiveness.

Study on the Application of Big Data Technology in Inventory Audit - Taking Swertia Audit as an Example

The arrival of the big data era provides new ideas for auditing to help transform traditional auditing into modern auditing. In this paper based on the existing literature, the basic data and application framework of big data auditing models are extensively reviewed, analyzed and summarized. The research and application of big data and its technology has become a domestic and foreign hot topic. In this paper, the application of big data technology in auditing is based on inventory analysis and auditing based on the analysis of the original auditing procedures and methods as well as the associated risks and difficulties. The case study of the Roe Deer Island scallop inventory audit event is used to study the possibility of applying big data auditing techniques in this area and the risk response in the case of the audit model, which provides a reference for the application of big data auditing in other inventory companies.

Application of Big Data in Tourism Destination Management: A Case Study of Changsha City

In the era of information technology, the utilization of big data technology is rapidly growing, leading to significant changes in the tourism industry. Big data not only creates more business opportunities for the industry but also drives the transformation and enhancement of tourist destinations and the implementation of efficient management. This study employs two research methods: literature review and case analysis. Firstly, by reviewing relevant literature, the latest research findings and trends in big data technology for tourism destination management are summarized. Secondly, through case analysis, a comprehensive understanding of the current situation and challenges in the application of big data technology in tourism destination management in Changsha is obtained. Leveraging the Changsha cultural and tourism data platform, this study retrieves information such as tourist reception data of tourism destinations in Changsha and assesses the impact of Changsha’s big data technology on tourism destination management. The research reveals limitations and challenges in the application of big data technology in Changsha’s tourism destination management, including data privacy protection and technical security, which require further exploration in future practices. The goal of this study is to offer insights for the application of big data in tourism destination management in Changsha and provide guidance for destination managers in similar cities.

Application of Big Data Technology in Audit

In the new era of rapid development of big data technology, the three key points of high-quality development of auditing are auditing talents, auditing rule of law and auditing technology. In the traditional audit process, auditors focus on professional judgment and professional competence, make audit plans, implement audit procedures and draw audit conclusions. However, there are many problems in traditional audit work, such as difficulty in obtaining sufficient external information and inefficiency in analyzing audit evidence. Therefore, auditors apply big data technology to audit work to solve various problems and put forward corresponding safeguard measures.

Application of Big Data Technology in College Music Education

Based on information technology to promote the upgrading of various industries, the core of Industry 4.0, the current internet big data has gradually become a hot spot of social development, and a huge amount of data gushes out. Music is the education of beauty, and it is a key to inspire people to be true and good. China has been a land of culture and music since ancient times, and has a very high position in the history of world music. Combining advanced information technology with traditional music education to promote the development of national music education is of great significance to quality education. Based on the high-speed development of the era of big data, this article studies the specific application of big data technology in college music education, helps college students to cultivate open thinking and improve their independent music learning ability through online teaching platform, and formulates different teaching plans according to students' music learning data.

Big data Research on the Application of Tracking Policy Audit in the Implementation of Tax Reduction and Fee

Since 2018, chinese government have conducted tracking audits on the implementation of tax reduction and fee reduction policies, facing the challenges of insufficient audit resources and massive unstructured data storage. This article attempts to introduce big data technology into tracking audit policies. This article elaborates on the importance and urgency of tracking and auditing the implementation of tax and fee reduction policies. The impact of big data technology in the field of auditing on the tracking and auditing of the implementation of tax and fee reduction policies is also discussed. It is proposed that auditing agencies should apply big data technology to the tracking and auditing of the implementation of tax and fee reduction policies. And we also attempted to design an audit plan based on Neo4j graphical database technology, which includes audit objectives and scope, audit content and focus, specific big data audit steps and audit results. In response to the problems that hinder the application of big data technology, suggestions have been put forward to improve the standardization of the data process system, actively promote more efficient big data technology, and increase the training of big data audit talents.

Exploring the Application of Big Data Technology in Macroeconomic Management and Strategy Formulation

Abstract The initiative of macroeconomic management and regulation helps to eliminate such undesirable phenomena as unfair competition and malicious competition in the market and effectively improves the fairness and transparency of the economic market. This paper takes the importance of macroeconomic management as a guide and analyzes the value of the application of big data technology in macroeconomic management. Taking China’s macroeconomic data from 2012 to 2024 as the research object, a macroeconomic trend forecasting model is established by combining the C4.5 algorithm of time series. Combined with the FAVAR model, the SV-TVP-FAVAR model is established to study the impact of macroeconomic policy uncertainty on the macroeconomic system. The model cumulative return, trend prediction, aggregate impulse effect, and stage response characteristics are used as indexes for model evaluation. The cumulative return of the macroeconomic trend prediction model can reach up to 82.29%, the incremental GDP is only 920 million yuan lower than the actual macroeconomic value, and the great value of the impact of quantitative tools and price tools on economic growth is 0.369 and 0.359, respectively. The analytical model of macroeconomic data is constructed on the basis of big data technology, which facilitates access to the trend of the macroeconomic changes and the influencing factors and provides a reference for the optimization of the macroeconomic management strategy to provide a reference.

The Application of Big Data Technology in the Educational Model of Psychological Parenting in Colleges and Universities in the Context of Social Media

Abstract In today’s rapid development of information technology, social media has become an indispensable part of the daily communication of students in colleges and universities. The issue of student mental health is increasingly receiving widespread attention from society. As an essential position for mental health education, how to effectively utilize big data technology for students’ psychological nurturing and enhance the pertinence and effectiveness of mental health education has become an important issue in education. In this article, by creating a four-level psychological parenting model built on a positive psychology perspective, we study extensive data mining, deep learning and other methods to analyze students’ social media usage data and identify students’ mental health status. For the 225 students in this article, the anxiety and depression self-assessment scale scores decreased significantly (P<0.05) after the psychological parenting education intervention, indicating that big data technology enhances the effect of mental health education in colleges and universities. Association rule mining revealed a distinct connection between psychological problems, such as learning pressure and maladaptation, and students’ social media behavior patterns. The application of big data technology in this study helps colleges and universities identify and intervene in students’ mental health problems more accurately, promoting comprehensive development.