Big Data Analysis Technology Research Articles

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

Public Authentic-Replica Sampling Mechanism in Distributed Storage Environments

With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%.

The role of big data analytics in the footwear supply chains: a multiple case study perspective

Purpose This paper aims to analyze the importance of big data analytics (BDA) and supply chain analytics for sustainable supply chain management in all types of enterprises in the footwear industry. The importance and implementation of BDA in supply chain management have been discussed in this research paper, and a conceptual framework is proposed for the implementation. Design/methodology/approach The previous research indicates that the application of BDA and supply chain analytics is the key point to establishing sustainable supply chain management. Even though there are several types of research on the implications of BDA, there is still a gap in practical implications in organizations. For a better understanding, comparison of frameworks has been done for literature support. Further, 11 in-depth, semi-structured interviews have been conducted with four organizations from the footwear industry, and a conceptual framework has been proposed. Findings This research demonstrates an important gap in the literature through a thorough investigation of how firms can improve operational efficiency and productivity through the framework application and whether it is feasible to apply this within the global footwear industry or not. In conclusion, the research has denoted that despite lack of understanding related to sustainability agendas and not having proper ERP or BDA systems, the organizations operating in Pakistan still met some standards of sustainability and are moving toward achieving more. Research limitations/implications The research has pointed out the importance of the integration of all stakeholders in the supply chain through a proposed framework that aligns and guides the stakeholders so that the policies and other constraints may be taken into consideration while revising sustainable supply chain strategies. Practical implications This study will help increase sustainability through the application of BDA technology, which will benefit various stakeholders, particularly those in the footwear industry. Originality/value This study has proposed a framework based on three stages of the implementation of BDA in the existing supply chain to maintain and achieve sustainability in the organization in the footwear industry. This three-stage framework covers many aspects that were not covered in the literature before.

Big Data Analytics in Gaza's Higher Education: Identifying and Addressing Key Implementation Barriers

This research provides an in-depth investigation into the adoption of Big Data Analytics (BDA) technology in higher education institutions (HEIs) in the Gaza Strip, along with a detailed analysis of the barriers to its implementation. Using a quantitative research methodology, data was collected from 305 participants. Principal Component Analysis (PCA) was employed to identify key barriers to BDA, including challenges related to infrastructure, security, resources, knowledge, and data characteristics. The study found that financial constraints, access pricing and conditions, significant investment costs, lack of BDA expertise, operational expenses, experience sharing, and resource development are the main barriers to BDA adoption. Additionally, the study shows that 65.3% of HEIs are using BDA technology, with a BDA readiness score of 66.68%. Regression analysis indicates that barriers related to security, finances, data characteristics, skills, and infrastructure negatively impact BDA practice and readiness. Based on these findings, proactive measures are recommended to address these barriers in the Gaza Strip. These measures include developing government initiatives, upgrading IT infrastructure, enhancing BDA skills, and promoting BDA technology awareness. The research advocates for further exploration of the specific challenges and opportunities faced by local universities and a deeper investigation into the potential benefits of BDA adoption. Overcoming these obstacles and fostering BDA integration could enhance data analysis capabilities, contributing to the growth and competitive strength of the Gaza Strip.

The Construction of Localized Teaching Model of Orff Music Teaching Method Based on Big Data Analysis

This paper discusses how Orff's music education system is combined with the localized teaching mode under the background of globalization, especially with the support of big data analysis to realize the innovation of music education. Orff's music education has attracted worldwide attention for its openness, diversity and innovation. In China, its localization practice aims to balance the international educational concept with the local cultural needs. The innovations of this paper are as follows: firstly, the big data analysis technology is integrated into the localized teaching mode, and the new mode of music teaching is analyzed; Secondly, advocate students' active participation, get rid of the shackles of traditional teaching and make music learning lively and interesting; Thirdly, from a new discipline perspective, combined with the frontier concept of national music education, this paper interprets Orff's localization theory and practice in order to push Orff's music education in China out of the dependent development mode.

Analysis of Profiles of Supporters of Conspiracy Narratives about Vaccination Against COVID-19 on a Social Network

The article analyzes the socio-demographic characteristics and value-worldview self-positioning of the distributors of conspiracy narratives (“conspiracy theories”) about vaccination against COVID-19 on the Russian social network “VK”. The research material consists of 33,674 profiles of VK users who published at least two messages during the pandemic that explicate the stated narrative. The collection and analysis of the material was carried out using big data analysis technologies. The purpose of the study is to determine whether it is possible to identify typical socio-demographic and ideological traits of anti-vaccer conspiracy theorists, which to a certain extent explain their penchant for conspiracy thinking. The results obtained refute the marginal dominance of conspiracy theorists in a pandemic situation. Versions popular in the scientific literature about the predominance of insufficiently educated people among “conspiracy theorists”, or the elderly as more gullible, or women as more suggestible, or residents of villages and small provincial towns as more conservative, etc. are not confirmed by the large-scale empirical studies of large user data. The results of the study may be useful for developing countermeasures and strategies to address public mistrust of institutions and official trends in public health, in particular vaccination policies. The traditional focus on information and educational measures (explanations about the dangers of the disease and the benefits of the vaccine) seems to be ineffective in the context of the emergence of value systems, cognitive distortions, general socio-political tension and other reasons that are still awaiting serious study.

Multivariate VAR system of regional economic growth under big data

Traditional research has problems such as limited data samples, limited variable selection, and insufficient real-time analysis capabilities. With the advent of the big data era, researchers are increasingly using big data analysis to discover the laws of development. This paper combines the VAR system with big data analysis technology to study regional economic growth variables, simulate economic changes in region A, and use the Markov chain Monte Carlo algorithm and historical simulation method to analyze the process of industrial change. The model accuracy of the two algorithms is evaluated by the historical data observation loss value of the VAR post-test. The results show that at a confidence level of 0.95, the risk prediction of the historical simulation method is more accurate. The study also found that adding additional variables to the VAR system will lead to model information loss and an increase in parameters.

Big data analytics capabilities and leadership: catalysts of firm performance in telecommunications

PurposeThe proliferation of big data analytics (BDA)-enabled tools and technologies has endowed organizations with the capacity to augment decision-making processes, optimize operational endeavors and foster innovation across diverse business domains. Consequently, BDA has been posited as a catalyst for enhanced customer relationship management, improved risk mitigation strategies and heightened operational efficiencies, all of which converge to augment overall firm performance. Thus, the purpose of this research is to introduce a conceptual framework aimed at explaining the influence of BDA capabilities on the performance of telecommunications firms in Pakistan. Additionally, it examines the potential mediating effect of talent capabilities and moderating effect of top management attitude on firm performance.Design/methodology/approachData from a sample comprising 520 participants were collected via survey questionnaires. The study employed Partial Least Squares-Structural Equation Modeling to empirically evaluate the proposed model.FindingsResults reveal a positive association between BDA technology and information capabilities with both BDA talent capabilities and firm performance. Furthermore, the analysis suggests that BDA talent capabilities mediate the relationship between BDA dynamic capabilities and firm performance, while top management attitude acts as a moderator, enhancing the relationship between BDA talent capabilities and firm performance.Originality/valueThere is a scarcity of research that has examined the relationship of BDA capabilities, top management attitude and firm performance. This study attempts to examine their interrelationships. First, it enhances the extant literature by elucidating the mediating role of BDA talent capabilities in the relationship between BDA technology and information capabilities and firm performance. Second, the study introduces a novel dimension by incorporating top management attitude as a moderator variable. This augmentation adds layers of complexity to comprehending BDA implementation dynamics, emphasizing leadership’s role in fostering an enabling environment for effective utilization of BDA capabilities.

Role of big data analytics and information processing capabilities in enhancing transparency and accountability in e-procurement applications

Purpose Considering information processing theory, this study aims to examine how big data analytics (BDA) mediates the influence of e-procurement coordination (EPC) and e-procurement transactional (EPT) applications on transparency and accountability (TA) in the procurement processes of firms within the United Arab Emirates' private sector. Furthermore, it investigates the moderating role of information processing capabilities (IPCs) in the relationships among EPC, EPT and BDA to clarify their collective impact on enhancing TA and procurement performance. Design/methodology/approach Data were collected from procurement and information technology professionals in the UAE’s private sector through a Web-based survey. Established scales were used to assess e-procurement, BDA, TA, procurement performance and IPCs. Data were analyzed using partial least squares structural equation modeling. Findings Integrating e-procurement with BDA demonstrates the potential to improve TA and procurement performance in the UAE’s private sector. BDA is positively associated with EPC and EPT applications use, contributing to increased procurement TA and enhancing overall procurement performance. Practical implications Organizations can enhance procurement TA by adopting e-procurement and BDA technologies. Originality/value This study identifies the mediating role of BDA in the relationship between e-procurement and procurement TA. In addition, it investigates the moderating role of IPCs in the relationship between e-procurement and BDA.

Application of thermal energy optimization based on improved neural network algorithm in green innovation capability evaluation of manufacturing enterprises

As the global focus on sustainable development continues to increase, manufacturing companies face the dual challenges of energy efficiency and environmental impact in the process of enhancing green innovation capabilities. The purpose of this study is to explore the application of improved neural network algorithm in thermal energy optimization, so as to improve the green innovation ability of manufacturing enterprises and promote their sustainable development. By constructing an improved neural network model and using big data analysis technology, this paper conducts in-depth analysis of thermal energy use in manufacturing enterprises, so as to identify and optimize energy consumption patterns. The study considered a variety of influencing factors, including equipment efficiency, production process and environmental policy, and evaluated the optimization effect through model training and testing. The experimental results show that the improved neural network algorithm can effectively identify thermal energy waste points, and put forward the corresponding optimization measures. After optimization, the energy use efficiency of manufacturing enterprises has been improved, carbon emissions have been significantly reduced, and the comprehensive evaluation score of green innovation ability has been improved, providing effective technical support for promoting the sustainable development of enterprises.

Research on monitoring and energy management systems for energy storage stations on the power generation side

Abstract Addressing the challenges of limited operational efficiency, inadequate battery safety, inflexible system scalability, inadequate AGC/AVC functionality, and low levels of intelligent operation and maintenance encountered by energy storage power stations integrated with new energy generation sources, this article delves into the pivotal role of these power stations on the generation side. It meticulously examines the network architecture, functional framework, and pivotal technologies underpinning the system. Furthermore, the article proposes a comprehensive monitoring and energy management system tailored specifically for energy storage on the generation side. The system uses micro-service architecture and container technology to realize the processing and storage of massive data. Through complete isolation of the information network and control network and millisecond communication with GOOSE, the charging and discharging response speed of the energy storage station is greatly improved. The power allocation algorithm based on device status recognition completes the system’s high-precision power control of PCS, and this system uses big data analysis and data mining technology to conduct a comprehensive statistical analysis of battery health status, power station operation efficiency, and equipment operation status and energy consumption. The design of the system has favorable technical and application guidance for the subsequent energy management design and engineering application of the energy storage power station.

Big Data Analytics for Financial Decision Making in Malaysia

Purpose: To aim of the study was to analyze the big data analytics for financial decision making in Malaysia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Big Data Analytics (BDA) significantly enhances financial decision-making in Malaysia by extracting valuable insights from large datasets. It helps improve risk management, customer segmentation, and operational efficiency for financial institutions. BDA enables predictive analysis of market behavior, supports personalized financial services, and enhances fraud detection capabilities. Despite its benefits, challenges like data privacy issues and the need for skilled data professionals persist. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & network externalities theory may be used to anchor future studies on big data analytics for financial decision making in Malaysia. Prioritize investments in scalable and secure data infrastructure to support the deployment and integration of BDA technologies across financial institutions. This includes upgrading data storage systems, enhancing data processing capabilities, and implementing robust data integration frameworks. Develop and enforce clear regulatory frameworks that govern the ethical use of BDA in financial decision-making. Regulatory guidelines should address data privacy, security standards, transparency in algorithmic processes, and consumer rights protection.

Harnessing Educational Big Data Analytics for Decision-Making in Enhancing School Teaching Quality

The application of educational big data analytics holds significant importance in enhancing decision-making processes for school teaching quality. This study explores the effective utilization of educational big data analytics technologies to support the improvement of teaching quality in schools. Initially, the challenges and needs faced by current school teaching quality decision-making were analyzed, highlighting the critical role of educational big data analytics in this context. Subsequently, the limitations and gaps in existing study were identified through a review of related studies, underscoring the study value of this study. Based on this foundation, this study progresses through an examination of the decision-making factors that influence school teaching quality, problem description and model assumptions, construction of decision models, and model solutions using genetic algorithms. By analyzing key factors and constraints in the decision-making process for school teaching quality and integrating optimization algorithms, a viable decision support model was proposed and empirically analyzed. This study aims to provide a scientific basis for school administrators and decision-makers, thereby promoting continuous improvement in school teaching quality.

KEY DETERMINANTS OF GOVERNMENT AUDITOR’S BEHAVIOUR TO ADOPT BIG DATA ANALYTICS IN AUDIT PRACTICE

<p><strong>Background</strong>: Despite the increasing adoption of big data and Big Data Analytics (BDA) in the business world, the accounting and audit profession is considered slow in taking advantage of these innovative developments. Government auditors play an important role indirectly in improving good governance. The use of BDA in government audits has been carried out in Indonesia since 2020. However, because its use is not mandatory, not all government auditors use the application.</p><p><strong>Objective</strong>: This research investigates the key determinants that drive government auditors' behaviour to adopt BDA technology in audit practice, especially in the Indonesian context, with a focus on the Indonesian Supreme Audit Agency (SAA).</p><p><strong>Research Method: </strong>This study was conducted through a questionnaire survey involving 126 government auditors in Indonesia. Meanwhile, research data was analyzed using the Structural Equation Modelling - Partial Least Square method (SEM-PLS).</p><p><strong>Research Results</strong>: Research findings show that performance expectancy and effort expectancy have a direct influence on auditors' intentions to use BDA techniques at SAA. Meanwhile, the auditor's intention to use BDA techniques has a significant effect on Actual Use.</p><p><strong>Originality/Novelty of Research: </strong>This research addresses the gap about<strong> </strong>factors that encourage auditors' behaviour to adopt BDA technology in audit practice, especially in public sector context.</p><p> </p>

Reimagining Teaching Style Through Renewable Energy Quality Assessment—A Focus on Power Technology and Equipment

This article delves into teaching style translation strategies from the perspective of power energy quality assessment, using power technology and equipment as specific cases, with the aim of enhancing the international dissemination efficiency of power energy professional knowledge in the education field. Through detailed data analysis, this article reveals the rapid pace of technological updates and equipment upgrades in the power energy industry, especially in the fields of smart grids and renewable energy. As of 2023, the proportion of non fossil energy generation installed capacity worldwide has approached 50%, indicating that the power industry is accelerating its transition to green and low-carbon. This article pays special attention to the complex terms and concepts involved in the teaching of power technology and equipment. Using the principle of adaptability in translation theory, it analyzes how to accurately convey the precise meanings of these terms in the translation process, ensuring that international students can accurately understand and master key knowledge points. By comparing traditional teaching methods with a new teaching model that incorporates elements of power energy quality assessment, the study found that the latter performs more outstandingly in improving students' practical and problem-solving abilities. Specifically, the accuracy of students in simulating power system fault handling has increased by about 30%. In addition, this article also utilizes big data analysis technology to comprehensively sort out and evaluate teaching resources in the field of power energy, and finds that high-quality teaching materials and case sharing are crucial for improving teaching effectiveness. Based on the above analysis, this article proposes a series of optimization suggestions for teaching style translation, in order to provide strong support for the international education of power energy majors and promote the exchange and progress of global power energy technology.

Application of Big Data Analysis and Cloud Computing Technology

Application of Big Data Analysis and Cloud Computing Technology

Study on the Application of Error Back-Propagation Algorithm Applied to the Student Status Management in Higher Education Institutions

The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization) algorithm are introduced. In addition, the LM algorithm is combined with the BR algorithm to optimize the BP neural network, so as to establish a prediction model of the employment rate of college graduates based on the LM-BP neural network (the established prediction model). Finally, the established prediction model is verified after the historical data of college students in the SSM are managed and processed using the big data analysis technology. It suggests that the established prediction model shows higher prediction accuracy, more stable prediction performance, more ideal prediction effect, and higher practical application value.

Implementation of Big Data Analysis Technology to Improve Decision Making in the Accounting Field

This article explores the Implementation of Big Data Analysis Technology to Enhance Decision Making in the Accounting Field. With the exponential growth of data, accounting professionals face challenges in processing and extracting meaningful insights. The study delves into the integration of advanced analytics tools within accounting practices to harness the potential of big data. The research begins by examining the current landscape of the accounting field and the traditional decision-making processes. It identifies the limitations and inefficiencies in handling vast amounts of financial data. Subsequently, the article elucidates the pivotal role of big data technologies in revolutionizing accounting practices. By employing sophisticated algorithms and data processing techniques, accountants can gain deeper insights into financial trends, risk factors, and performance metrics. Moreover, the study investigates real-world implementations of big data analytics in accounting firms. It highlights success stories and challenges faced during the transition, shedding light on the practical implications of adopting such technologies. The article emphasizes the importance of skill development among accounting professionals to effectively navigate and utilize big data tools. Furthermore, the impact of big data analysis on decision-making accuracy and efficiency is scrutinized. The findings underscore the potential for improved financial forecasting, fraud detection, and strategic planning. The integration of big data analytics is positioned as a catalyst for more informed and timely decision-making in the dynamic landscape of accounting. In conclusion, this research contributes to the evolving discourse on the intersection of big data technology and accounting. It provides valuable insights into the transformative potential of implementing advanced analytics tools, ultimately enhancing decision-making processes within the accounting field.

Вопросы внедрения аналитических систем больших данных и других достижений цифровизации для повышения эффективности бизнеса горнодобывающих компаний

Currently, digitalization and widespread adoption of digital technologies are significantly changing people's activities in many areas. Digital technologies provide automation of business processes, data management, analytics, they support strategic decision-making and dictate the need to introduce new approaches to doing business in order to increase its efficiency and profitability, as well as to ensure sustainability of companies' development in modern conditions. It needs to be especially mentioned how the big data processing and analysis technologies and other Industry 4.0 achievements are introduced in the mining industry. The use of big data analytical systems in modern production, including the mining industry, provides an integrated approach to processing and analyzing a large amount of information. It also provides organizations with significant advantages reflected at various levels of management and strategic decision-making. The prospects for implementation and development of these digital solutions currently look very encouraging. Effective management of these processes provides companies with significant opportunities and advantages, allowing them to increase competitiveness, optimize the use of resources and increase the efficiency of their business as a whole.

Application and Innovation of Information Technology in Rail Transit Courses in Higher Vocational Colleges

The innovation of information technology in the field of rail transit mainly includes big data analysis and intelligent decision making and the application of artificial intelligence technology. Through big data analysis and intelligent decision-making technology, the operational efficiency and passenger experience of rail transit can be improved. Artificial intelligence technology can realize intelligent functions such as train operation prediction, passenger demand analysis, and safety monitoring, so as to improve the safety and reliability of the rail transit system.