Big Data Analysis System Research Articles

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

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.

Digital Transformation in Talent Acquisition: An Investigation into the Integration of Big Data Analytics and Management Information Systems for Optimal Hiring Strategies

The primary goal was to empirically investigate the relationships amongst Big Data Analytics (BDA), Management Information System (MIS), optimal hiring strategies, leadership support, and talent acquisition process. This research aimed to fill crucial gaps in the literature by examining how technology adoption and leadership support influence the formulation and effectiveness of optimal hiring strategies, finally impacting talent acquisition results in the Chinese business landscape. Quantitative research was employed, targeting experts and decision-makers in human resource departments across numerous industries in China. The research used a random sampling approach to acquire facts through an online questionnaire, examining responses from 168 participants. Regression and mediation analyses, facilitated by SPSS, were applied to discover the relationships and mechanisms among BDA, MIS, optimal hiring strategies, leadership support, and talent acquisition success. The results discovered a significant and positive relationship between BDA and MIS with the optimal hiring techniques. Moreover, optimal hiring techniques have proven an advantageous association with talent acquisition success. The research similarly showed the mediating role of optimal hiring strategies in translating the advantages of BDA and MIS into talent acquisition success. Leadership support emerged as a moderator, amplifying the relationship between optimum hiring techniques and talent acquisition success. This study contributes empirical proof to the underexplored intersection of leadership support, technology, and talent hiring strategies. The findings offer practical insights for companies aiming to navigate the complexities of recruitment, emphasizing the significance of strategic decision-making, technology integration, and management endorsement in achieving talent acquisition success.

The Technology Evolution and Future Trend of Smart Blackboard

As an important tool for educational digitization, the function of the smart blackboard has gradually evolved from a single teaching information-sharing tool to a smart system for knowledge expression, sharing and generation. Smart blackboard technology has been experienced from chalk + blackboard to projection equipment + blackboard, to interactive electronic whiteboard, and then to today's integrated touch, high-definition display and artificial intelligence technology. The evolution process of the smart blackboard is characterized by interactivity, effectiveness and intelligence. At present, the smart blackboard has become a collection of many advanced technologies. It is gradually integrated into modern technologies such as artificial intelligence, cloud computing, big data analysis and the Internet of Things, making remote collaboration and intelligent classroom management possible. It has become a smart system for big data collection, mining and analysis, evaluation and feedback, and teaching decision-making. Looking forward to the future, the smart blackboard will pay more attention to user experience design, provide an immersive learning experience through Augmented Reality and Virtual Reality technology, and optimize personalized teaching content in combination with machine learning, to lay a foundation for building an intelligent and personalized teaching environment.

The Effect of Supply Chain Management Practices on Competitive Advantage in Manufacturing Industry in Pakistan: The Moderating Role of Big Data Analytics

The purpose of the current study is to examine that what big data analytics (BDA) may have a moderating effect on the relationship between supply chain management practices (SCMPs) and competitive advantage (CA) in Pakistani manufacturing companies. Data was gathered quantitatively from several manufacturing businesses located industrial in Karachi. The study hypothesis was tested using SPSS method and linear regression analysis, sample size is 154 persons working in small, medium and large sized manufacturing companies with multiple categorizations based on enterprise size, job & experience. The findings demonstrate that SCMPs have a major favorable impact on competitive advantage (CA). In particular, information quality (IQ), customer relationship management (CRM), and strategic supplier partnerships (SSP) all significantly improve competitive advantage (CA), information sharing (ISh) not significantly on competitive advantage (CA). Furthermore, investigates the association between SCMPs and CA in Pakistani manufacturing enterprises, as well as the moderating effect of big data analytics (BDA). The findings validate H1, H2, and H3 and demonstrate the beneficial effects of Supplier Strategic Partnership (SSP), customer relationship management (CRM), and information quality (IQ) on competitive advantage (CA). This study is to look at how big data analytics (BDA) may be able to moderate the relationship between supply chain management practices (SCMPs) and competitive advantage (CA) in Pakistani manufacturing companies. The findings demonstrate that SCMPs significantly improve CA. Information quality (IQ), customer relationship management (CRM), and strategic supplier partnerships (SSP) in particular significantly improve competitive advantage (CA). Information sharing (ISh), on the other hand, had no effect on competitive advantage (CA). Some flaw in the way the firms under investigation handle big data analytics (BDA). It emphasizes that for big data analytics (BDA) systems to produce the intended outcomes, managers must integrate them with other business systems and capabilities.

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

Hospital Medical Behaviour Supervision and Operational Efficiency Evaluation Method based based on Big Data Platform

The application of cloud computing and big data core technologies and concepts to medical informatization can improve its flexibility and efficiency, and achieve the overall deployment and intensive management of the system. In the era of big data, the use of information management platform can optimize and standardize clinical diagnosis and treatment process, improve the quality and efficiency of diagnosis and treatment services, and improve the quality and level of scientific research, which meets the requirements of medical reform for fine management of hospitals and precise medical treatment in the era of evidence-based medicine. This paper introduces the development and application of big data analysis in the field of information technology. Through the research on the supervision of hospital medical service behavior, the top-level design is used to standardize the content of medical behavior supervision, and the effectiveness of supervision is discussed to achieve the purpose of reducing clinical paperwork. Based on the needs, the medical service behavior supervision system is proposed and constructed. Strengthen hospital medical behavior supervision through hospital big data analysis and knowledge base system support. This system can provide management with an effective monitoring and management tool, enabling them to promptly identify and solve problems that arise in medical services. By analyzing a large amount of medical data, hospitals can help predict the trend of disease occurrence in advance, thereby taking preventive and control measures in advance, and reducing the occurrence and spread of diseases. Through deep learning and analysis of patient data, personalized treatment plans can be provided for each patient to improve treatment effectiveness.

Retraction Note: Intelligent Crime Prevention and Control Big Data Analysis System Based on Imaging and Capsule Network Model

Retraction Note: Intelligent Crime Prevention and Control Big Data Analysis System Based on Imaging and Capsule Network Model

Exploring the role of big data analytics and dynamic capabilities in ESG programs within pharmaceuticals

Achieving the United Nations’ Sustainable Development Goals (SDGs) requires environmental, social, and governance (ESG) programs in the pharmaceutical industry. Using the Millennium Development Goals, the 2030 agenda aims to transform European Union companies toward sustainability. In pharmaceuticals, in particular, ESG programs come with complexities such as employee skills, corporate goals, and management expectations. Managing these programs effectively requires advanced technologies such as big data analytics (BDA) and dynamic capabilities (DC). In this study, DC theory is used to develop an architecture for managing ESG criteria, focusing on provenance, traceability, and availability. BDA’s role in ESG programs is explored, along with its use cases and benefits, and how DC drives success in ESG implementation. The study examined five pharmaceutical companies in Germany, Portugal, and Switzerland, all consulting the same firm for BDA systems, to identify the characteristics of effective BDA implementation. The research explores how BDA and DC jointly enhance ESG efforts, the essential skills needed, and how DC aids in real-time decision-making in BDA projects aligned with ESG standards. It highlights the BDA system’s accuracy and effectiveness in managing ESG programs, with DC as a pivotal facilitator. Findings reveal BDA’s value in operational efficiency and aligning business models with ESG goals, underscoring the need for diverse skills in BDA implementation and DC’s importance in integrating various managerial capacities into effective strategies. The study promotes a dynamic, data-driven approach in the pharmaceutical industry for managing complex ESG initiatives. It stresses continuous learning, adaptation, and integrating technological advances with ethical business practices. The research concludes by emphasizing BDA and DC’s vital roles in advocating ethical, socially responsible, and environmentally sustainable practices in the pharmaceutical sector, marrying technology with ethical business strategies.

A Resource-Based View Assessment of Big Data Analysis and Its Impact on Strategic Human Resources Quality Management Systems

Big data analysis is a process to analyze all the structured and unstructured and semi structured data collected from different types of sources. Therefore, collecting proper data and information from different sources is very important for any business organization and analysis of those data is also important. Thus, collecting information from different sources helps to gain knowledge about the demands and needs of the customers and market. Whereas analyzing all the data helps to understand the market trends which are helpful to enhance business performance. Apart from that, human resource management is another important system of any business organization. Therefore, human resource management is a strategy process or system which helps to enhance the productivity of the business organizations. Along with that there is a big impact of big data analysis on the human resources management system and the impacts are analyzed in this particular research study. On the other hand, the purpose of this research study is to analyze the concept of resource based big data analysis. Apart from that, the impact of big data analysis on the strategic human resource management systems of the business organizations is another purpose of this particular research study. Thereafter the researcher has adopted several types of techniques and methods for collecting more information and analyzing all the data. Furthermore, research has used the secondary methods and the sources for collecting some accurate and genuine data about big data analysis and human resource management systems. Apart from that the research has used the qualitative techniques for analyzing all the collected data in a systematic review. Therefore, the uses of secondary sources help the researcher to gain knowledge about the process of big data analysis and the process of human resource management. Thus, impact of big data analysis on the human resource management system is analyzed in this particular research study.

Review of Agile SDLC for Big Data Analytics Systems in the Context of Small Organizations Using Scrum-XP

Software development using agile System Development Life Cycles (SDLC), such as Scrum and XP, has gained important acceptance for small businesses. Agile approaches eliminate barriers to required organizational, technical, and economic resources usually necessary when rigorous software development approaches, through heavyweight methodologies (e.g., Rational Unified Process (RUP)) or heavyweight international standards (e.g., ISO/IEC 12207) are used. However, despite their high popularity in small businesses, their utilization is scarce in the emergent domain of Big Data Analytics Systems (BDAS). Consequently, small businesses interested in deploying BDAS lack systematic academic guidance regarding agile SDLC for BDAS. This research, thus, addresses this research gap, and reports an updated comparative study of three of the main proposed SDLCs for BDAS (Cross-Industry Standard Process for Data Mining CRISP-DM), Two mains were Microsoft Team Data Science Process (TDSP), and Domino Data Science Lifecycle (DDSL)) in the current BDAS development literature, against a Scrum and Extreme Programming (Scrum-XP) SDLC. For this aim, a Pro Forma of a generic Scrum-XP SDLC is used to examine the conceptual structure, i.e., roles, phases-activities, roles, and work products-of these two SDLCs. Hence, this comparative study provides theoretical and practical insights on agile SDLC for BDAS adequate for small businesses and calls for further conceptual and empirical research to advance toward an agile SDLC for BDAS supported by academia and used in practice.

Research on the application of network security defence in database security services based on deep learning integrated with big data analytics

Every day, more people use the internet to send and receive sensitive information. A lot of confidential data is being transmitted electronically between people and businesses. Cyber-attacks, which are the inevitable result of our growing reliance on digital technology, are a reality that we must face today. This paper aims to investigate the impact of Big Data Analytics (BDA) on information security and vice versa. Additionally, an Artificial Neural Network (ANN)-based Deep Learning (DL) method for Anomaly Detection (AD) is presented in this work. To improve AD, the proposed method uses a DL-based detection method, which is used to parse through many collected security events to develop individual event profiles. The paper also investigated how BDA can be used to address Information Security (IS) issues and how existing Big Data technologies can be adapted to improve BDA's security. This study developed a DL-based Security Information System (DL-SIS) using a combination of event identification for data preprocessing and different Artificial Neural Network (ANN) methods. The feasibility and impact of implementing a Big Data Analytics (BDA) system for AD are investigated and addressed in this study. From this study, we learn that BDA systems are highly effective in securing Critical Information Setup from several discrete cyberattacks and that they are currently the best method available. By analyzing the False Positive Rate (FPR), the system facilitates quick action by security analysts in response to cyber threats. DL-SIS had the highest AD accuracy of 99.40% but performed poorly in the high-dimensional dataset.

Development of a Big Data Analysis and Management Decision Support System for Student Mental Health in Higher Education

Abstract In recent years, the number of psychological problems occurring in college students has been increasing day by day, and timely, convenient, and accurate psychological warning is an important way to prevent college students from psychological crises. Starting from the basic needs of the system and aiming at realizing the embedding of data mining technology in the psychological management system, this paper elaborates on the design and implementation of the data mining technology module suitable for the psychological management system. The clustering algorithm-K mean algorithm of data mining is used to distinguish groups of students with different categories of psychological problems, which provides data support for the next decision-making. Through simulation experiments between the system designed in this paper and four other mental health management systems, the clustering performance and system evaluation performance of this paper’s system are better than those of the other systems. The result of mining the mental health of college students through this system shows that 82.2% of the student’s assessment results show normal, and 8.35% of the students need to focus on, and this result is not much different from that obtained from the statistical survey of a personality questionnaire, which indicates that the use of clustering analysis in the system can be an effective prediction of the mental health of college students.

외래어의 형태적ㆍ의미적 특성 연구

The purpose of this study is to examine the morphological and semantic characteristics of loanwords of Korean newspaper headliness. In order to analyze the recent use of loanwords, newspaper headlines in sports sections from January 1, 2023, to September 30, 2023, were analyzed. A total of 2,049 newspaper article titles were initially extracted using “K-League” as a key search term in Big Kinds, a big data analysis system for news provided by the Korea Press Foundation, and then the titles of the newspaper articles were checked and a list of loanwords was compiled. As a result, a total of 125 loanwords appeared. Several signifi cant features of loanwords are discovered in this study. Loanwords that appear in the newspaper headlines are single words and complex words. Complex words are created by derivation, compounding, and clipping. Loanwords are combined with other loanwords, native words, and Chinese characters to form compound words, and foreign words are used as morphemes to expand the vocabulary and create new loanwords.

Big Data Analysis and Decision Support System Based on Deep Learning

Big Data Analysis and Decision Support System Based on Deep Learning

A multi-agent based big data analytics system for viable supplier selection

A multi-agent based big data analytics system for viable supplier selection

민간조사제도에 대한 빅데이터 분석 연구

In this study, we would like to explore how the media means the private investigation system through “Big Cains,” a news big data analysis site. Therefore, it provided a big data system for the media and collected data from various media organizations to analyze media reports on private investigations through BigKinds, a news big data analysis system created as news content. In order to achieve the purpose of the research, the flow of keywords was identified by dividing them into 2000s, 2010s and 2020s and deriving and presenting the number of related articles every year during that period. As a result of this study, it was found that in the 2000s, the frequency of media reporting was low at a time when public interest in the private investigation system and various studies were not conducted in academia. Second, in the 2010s, there was a high topic rank on the private survey system compared to the 2000s, and various research and education were conducted in academia and industry related to government policies and private surveys. Third, the 2020s marked a high topic rank as related laws were revised, heralding a new change in the private investigation system. Therefore, it is hoped that the government will move in a direction that will greatly help the people's security demand by supplementing the protection of citizens' rights, minimizing conflicts between direct translations, and limitations of national security.

LETHE: A Digital Intervention for Cognitive Decline.

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform development.Clinical Relevance - Patient Management Systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

Creating relevant content using machine learning methods

Today, users generate various data when working on the Internet. As a result, there are large amounts of different information. This information can be useful for both regular users and large com-panies. One of the problems with the use of accumu-lated information is its crude, unstructured nature. In addition, different user groups do not need the entire data set, but their own target sample. To solve this problem, various software tools have been developed today. This work is devoted to solving the problem of creating relevant information sets from large arrays of data for its further use by the target audience. Goal. The development of big data analysis systems is carried out to obtain new, previously unknown information. The methodology of applying algo-rithms of work with large data sets and methods of machine learning is used, namely the pandas library for operations on a data set and logistic regression for information classification As a result, a system was built that allows the analysis of lexical infor-mation, translate it into numerical format and create on this basis the necessary statistical samples. The originality of the work lies in the use of specialized libraries of data processing and machine learning to create data analysis systems. The practical value of the work lies in the possibility of creating data analy-sis systems built.

Study of trip card data analysis system under big data

Abstract Digital technologies such as the Internet, big data, cloud computing, mobile communication, and artificial intelligence have played an important role in the prevention and control of this new crown epidemic. Driven by big data technology, collaborative governance in the digital era has injected new dynamics for collaborative governance in the general sense, especially in this new crown epidemic prevention and control shows a different governance logic from the past, and the refinement and extraction of this governance logic is the key to enhance the ability of new crown epidemic prevention and control. Compared with the previous model in which a single governmental governance body played a role. In this paper, the necessity of building a big data analysis system is clarified by analyzing the limitations of the data analysis means of the existing trip code system. The overall epidemic prevention advantage of adopting the big data trip code policy has increased by 51.95% for regions with high crisis risk level and strong economic power, such as Guangdong, Zhejiang and Shanghai, which need to face the risk spread from domestic and imported risks under the big data trip code control, the increase of new cases compared to the previous comparison has decreased by 68.5%, which can visually see the big data trip code epidemic prevention We can visually see the effectiveness of big data trip codes in preventing the epidemic and making excellent contributions to the global fight against the epidemic.