Application Of Big Data Technology Research Articles

Research on Piano Program Programming and Creative Optimization Methods Based on Big Data Technology

This study focuses on the programming and creative optimization methods of piano tracks based on big data technology. Through in-depth analysis of the potential application of big data technology in the field of music creation, we propose an innovative framework for piano repertoire programming and creative optimization. This framework fully utilizes the data processing and analysis capabilities of big data technology to mine massive piano repertoire data, extract music elements, style features, and creative patterns. In terms of piano repertoire programming, we use big data technology to quantitatively analyze music elements and construct a data-driven programming model. This model can automatically generate piano repertoire frameworks that match specific styles, emotions, and rhythms based on user creative needs, providing inspiration and direction for creators. In terms of creative optimization, we utilize machine learning algorithms based on big data technology to intelligently evaluate and improve piano tracks. By comparing and analyzing the advantages and disadvantages of different tracks in terms of melody, harmony, structure, etc., we have discovered and extracted the common characteristics of excellent works, providing guidance for creators to optimize their work. This article will focus on the programming and control capabilities of music programming in the design and composition process of big data technology, as well as optimization methods for music creation. Provide precise services for music teaching and research, promote the prosperity and development of music, and accelerate the integration and innovative development of information technology and music education and teaching. In terms of piano repertoire programming, we utilized big data technology to quantitatively analyze music elements and constructed a data-driven programming model. This model can provide creators with rich inspiration and clear direction based on their creative intentions and specific styles. In addition, our study also considered the recognition rates of training models for different instrument types. Through training and optimizing models, we have ensured that piano program programming and creative optimization processes can achieve high recognition rates and accuracy on different instrument types, thereby further expanding the application scope of big data technology in the field of music creation.

An analysis of risk control in the financial sector using big data technology

The rapid development of information technology has ushered in a new era of big data in the financial industry, which has provided financial institutions with a plethora of novel tools for risk management, while also playing a pivotal role in the industry's risk control landscape. This paper analyzes the public data of existing financial institutions, combines relevant literature, and employs big data technology to assess and identify risks, which aims to explore how big data technology can improve the risk management ability of the financial industry. In addition, the paper examines the challenges and problems faced by big data technology in the application of financial risk control, including data privacy and security, technology costs, and the demand for specialized talents. The research demonstrates that big data technology improves the speed and accuracy of risk identification, but also points out the challenges of data privacy and security management. Future risk control models need to better integrate big data analytics with traditional risk management methods, thus promoting the further development and application of big data technology in the field of risk control.

Online and Offline Integration Scheme of College English Education Under Big Data Technology

How to use big data technology to promote the integration and complementarity of English online education and offline education has become an urgent problem in the field of higher education. This paper mainly studies the integration and complementary strategies of online and offline English education in higher vocational colleges based on big data technology. This paper first analyzes the current situation of English teaching in higher vocational colleges, then discusses the application of big data technology in online English education in higher vocational colleges and puts forward relevant strategies of integration and complementarity. Finally, it expounds the important role of big data technology in promoting the integration and complementarity of online and offline English education in higher vocational colleges. In the future, we can further strengthen the application depth and breadth of big data technology in higher education, so as to promote the reform and development of English teaching in higher vocational colleges and improve the teaching quality.

Research on Load Decomposition and Optimization of Intelligent Elderly Care Service Based on Heuristic and Event Detection Algorithm

Against the backdrop of the digital age, the openness, equality and interaction of the Internet economy have injected new vitality into China’s traditional industries. The application of big data technology, especially in information integration and analysis, has become a key force in promoting the sustainable and healthy development of the national economy. This study focuses on the “Internet +” environment, discusses the impact of the aging problem of community workers on home care services, and proposes an optimization scheme based on a heuristic algorithm. The heuristic algorithm, inspired by the foraging behavior of ants in nature, optimizes the route selection problem by simulating an ant colony to choose the path with a high concentration of pheromones and shows outstanding application potential in the field of home care. The accuracy of the event detection algorithm is directly related to the performance of the load decomposition algorithm, and the change point detection algorithm can effectively identify the change point of the probability distribution in the time series data, which provides important input data for unsupervised clustering. Advanced computer theory, including the Hidden Markov model (HMM) and swarm intelligence optimization algorithm, is used in this research. By comparing different swarm intelligence algorithms, we find that the standard Gray Wolf optimization (SGWO) model is better than the basic Gray Wolf optimization (BGWO) algorithm and the improved Gray Wolf optimization (DGWO) algorithm in terms of stability and output results. The SGWO model significantly improves the efficiency of the load decomposition algorithm, which has been verified in the application of the smart elderly care service platform. The platform not only supports the operation of related technologies and information products but also realizes the seamless integration of information among various subjects of elderly care services. In addition, the factor hidden in the Markov model that can be selectively activated effectively monitors equipment status in the Internet of Things environment, provides real-time monitoring of user consumption behavior and fault information and further enhances the quality and efficiency of smart elderly care services.

Application of big data technology in the medical field

This project aims to construct a knowledge graph system applied to the field of traditional Chinese medicine (TCM) by extracting entities (such as drugs, diseases, etc.) and their relationships from TCM medical case data and storing them in a Neo4j database. The project process includes data reading, entity recognition and extraction, data formatting, and data import into the database. The project not only improved the individual's proficiency in Python data processing techniques (including regular expressions and JSON parsing) but also enhanced their skills in knowledge graph construction and database operations. In the future, there is a desire to further improve technical capabilities, explore more cutting-edge technologies in the TCM field, and promote project progress through collaboration, contributing to the modernization of TCM and intelligent healthcare services.

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.