Application Of Big Data Analytics Research Articles

The Effect of Big Data Analytics on Predictive Policing: The Mediation Role of Crisis Management

Objective: The objective of this study is to evaluate the impact of big data analytics (BDA) on predictive policing, particularly examining the mediating role of crisis management in this relationship. Theoretical Framework: The research is anchored in the domain of big data analytics, focusing on its application within law enforcement for enhancing predictive policing capabilities. The study explores how crisis management serves as a linkage between data analytics and predictive policing practices. Method: The study gathered data from 450 individuals working across various police departments in Dubai, utilizing a questionnaire to collect responses. The analytical approach was based on Structural Equation Modeling, conducted using AMOS software. Results and Discussion: Findings from the research indicate that big data analytics significantly boosts predictive policing and crisis management. Importantly, crisis management was identified as a mediating factor between big data analytics and its efficacy in predictive policing. These results suggest that big data analytics not only directly enhances predictive policing but also improves it indirectly through effective crisis management. Research Implications: This study underscores the importance of integrating big data analytics into police operations to advance predictive policing capabilities. It highlights the dual benefits of big data analytics in both direct application and enhancement through crisis management processes. Originality/Value: This research contributes to the limited but growing body of literature on the application of big data analytics in predictive policing. It offers practical guidelines for police forces, especially within the UAE, to better harness big data for improving their operational effectiveness and crisis management strategies. The study also discusses broader implications for both practice and ongoing research in this evolving field.

Development Of English Education Using Big Data And Learning Analytics

With English education using big data and learning analytics represents a transformative approach to enhancing language learning outcomes and pedagogical practices. By harnessing the power of big data analytics, educational institutions can collect, process, and analyze vast amounts of learner data, including student interactions with digital learning materials, performance on assessments, and engagement metrics. Through sophisticated learning analytics techniques, educators gain valuable insights into students' learning behaviors, preferences, strengths, and areas for improvement. The paper explores the application of big data analytics and learning analytics in English language education within Chinese universities. Utilizing a diverse dataset, including proficiency levels, engagement metrics, and demographic characteristics, the study employs Median Clustering Fuzzy Learning Analytics (MCFLA) to predict students' engagement levels based on language proficiency scores. Findings reveal varying proficiency levels among students, alongside mixed patterns of engagement. The integration of MCFLA proves effective in identifying trends and guiding educational interventions. Median scores provide benchmarks for assessing performance, while demographic factors offer additional insights. Findings reveal varying proficiency levels among students: 40% beginner, 30% intermediate, and 30% advanced. Mixed patterns of engagement are observed, with 50% high engagement, 30% medium, and 20% low. Integration of MCFLA effectively predicts engagement trends, with 75% accuracy. Median scores provide benchmarks for assessing performance: beginner (Vocabulary: 63, Grammar: 66, Reading: 68, Oral Communication: 55), intermediate (Vocabulary: 78, Grammar: 79, Reading: 80, Oral Communication: 72), and advanced (Vocabulary: 90, Grammar: 88, Reading: 95, Oral Communication: 80). Demographic factors such as gender (50% male, 50% female) and socio-economic status (40% middle class, 30% upper class, 30% lower class) offer additional insights.

Design and Application of Decision Support System for Educational Management Based on Big Data

Decision support system (DSS) for educational management based on big data leverage advanced analytics and machine learning techniques to enhance decision-making processes within educational institutions. By collecting and analyzing large volumes of data from various sources such as student performance, administrative records, and institutional resources, the DSS provides valuable insights and predictive models to administrators and educators. These insights can inform strategic planning, resource allocation, student interventions, and curriculum development, ultimately improving student outcomes and organizational efficiency. With the ability to process vast amounts of data in real-time, the DSS enables timely and data-driven decision-making, empowering educational leaders to address challenges and capitalize on opportunities in today's complex educational landscape.The integration of big data analytics with the Recommender Ranking Decision Support System (RRDS) presents a transformative approach to enhancing educational management. This paper explores the application of big data analytics within the educational setting, focusing on the development and implementation of the RRDS. By leveraging large and diverse datasets, the RRDS enables personalized learning experiences, optimizes resource allocation, and enhances student outcomes. Through predictive modeling, the RRDS predicts student performance and identifies at-risk students, facilitating targeted interventions to support student success and retention. Additionally, the system offers personalized learning recommendations tailored to individual student needs, fostering engagement and motivation. The findings underscore the significance of data-driven decision-making in improving educational practices and institutional performance. As educational institutions increasingly embrace technology-driven solutions, the integration of big data analytics and RRDS stands poised to revolutionize educational management, ultimately leading to more effective teaching and learning methodologies and better outcomes for students.

Big data security and privacy in healthcare: A systematic review and future research directions

Big data security involves protecting healthcare data from unauthorised use and access and making any changes, while privacy entails ensuring the confidentiality of individual patient information within the datasets. The two are crucial in ensuring that big data is effectively applied for research and personalised care, compromising the sensitivity of healthcare data and medical information. Consequently, as the amount of data in the healthcare sector grows exponentially, securing sensitive data becomes increasingly crucial while effectively utilising big data analytics. The systematic review explores the issues and challenges associated with big data security and privacy in healthcare. Through reference to resource-based view theory, this paper seeks to examine the present state of research in this area, identify gaps in the existing literature, and propose strategies for future research. The search strategy looked at the data between 2013 and 2022 and yielded 93 studies from four databases: Scopus, PubMed, ScienceDirect and IEEE Xplore. From these studies, 18 papers are reviewed. The findings reveal that while significant progress has been made in securing healthcare data, numerous challenges still need to be considered. Overall, this review highlights the importance of continued research to ascertain that sensitive healthcare data is kept secure while allowing for the enhanced application of big data analytics. It contributes to the security and privacy problems associated with using big data in healthcare, measures for addressing these challenges, the healthcare value of big data, and foci for future research.

Analyzing the Efficacy of the Relative Strength Indicator of Capital Inflows and Outflows Based on Big Data Analysis in Achieving Abnormal Returns Evidence from the Chinese Stock Market

This study originates from an analysis of market microstructure and introduces a novel statistical indicator of relative strength in capital flows through the application of big data analytics. This indicator effectively captures the impact of capital movements on future stock prices by integrating variations in stock prices with the volume of transactions within a corresponding timeframe. Building upon this foundation, the research develops an innovative momentum investment strategy based on the relative strength indicator of capital inflows and outflows, extending beyond the traditional fixed holding period momentum strategy framework. Empirical analysis reveals that this new indicator significantly demonstrates momentum effects in the Chinese A-share market; notably, in portfolios of stocks with smaller market capitalizations, the momentum strategy grounded on the new indicator achieves higher excess returns compared to traditional momentum strategies. These findings not only validate the efficacy of the new indicator but also underscore the critical role of big data analytics in enhancing the analysis of financial markets and the efficiency of investment decision-making.

Research on the Influence of Network Culture Group on College Students' Ideological and Political Education from the Perspective of Big Data Analysis

Today's society has entered the era of big data. Big data technology has been applied to ideological and political education in colleges and universities. The application of big data analysis in the field of ideological and political education in colleges and universities can promote the inclusive expansion, individualized teaching and expansion of ideological and political education in colleges and universities. Internet new media provides unprecedented space and potential for cultural innovation in the youth circle, and expands the path of production, dissemination, consumption and participation of cultural individuals and groups. The network culture circle group has gradually emerged and developed into the main place for the breeding and spread of network public opinion, which has an important impact on the ideological and political education of college students. From the perspective of big data analysis, colleges and universities need to do a good job in reshaping educational concepts, improving political positions, enhancing public opinion monitoring, and strengthening position management, and explore the path of ideological and political education for college students from the perspective of network cultural circles, so as to do a good job in ideological and political education for college students.

Application of Big Data Analysis and Intelligent Algorithm in Power System Operation Optimization

The power communication system provides powerful technical support for realizing the intelligent operation and information management of the power grid and improving the operation efficiency and power supply quality of the power grid. Quantum key distribution (QKD) is considered one of the most promising technologies for commercialization. QKD uses a single photon to encrypt data to produce a more secure and reliable password. This paper intends to study the hierarchical, centralized control architecture of power dispatching based on quantum essential supply (QKD). The performance indexes of MDI-QKD under symmetric and asymmetric conditions were studied by local optimization. The optimal key formation rate of the algorithm is analyzed. From the perspective of quantum critical utilization, a quantum key utilization scheme for grid backbone dispatching service is proposed. The dynamic adjustment test of multi-node time slot and service key update rate is carried out. Experiments show that the MDI scheme can effectively improve the effectiveness of a multi-node QKD system. Thus, the security of data transmission of the core business of power dispatching data networks can be ensured to the greatest extent. AMDI can effectively reduce the transmission timeout of low-priority data streams because the delay of high-priority data streams reaches the proportion. It can be an excellent solution to the power system and the password requirements.

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.

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment’s findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

Analysis and Application of Big Data in Social Media Marketing

This paper discusses the importance and advantages of big data analysis and application in social media marketing. With the popularity of social media platforms, big data analysis provides enterprises with opportunities to deeply understand user needs, optimize marketing strategies and improve marketing effects. This paper introduces the current situation of social media marketing, and expounds in detail the application of big data analysis in user portrait analysis, user behavior analysis and marketing effect evaluation. Through big data analysis, enterprises can formulate more accurate marketing strategies, improve marketing accuracy, optimize user experience and improve marketing efficiency. However, big data analysis also faces challenges such as data quality and privacy protection, which requires enterprises to pay attention to data security and compliance in the process of application.

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

In the digital era, manufacturing industries confront challenges like heightened global competition and intricate production processes, urging them to boost efficiency and productivity. Amidst these circumstances, Big Data emerges as a pivotal opportunity to enhance manufacturing performance. Big Data, characterized by vast volumes of data, utilizes advanced data mining to machine learning techniques for analysis. Data analytics, an interdisciplinary field, profoundly impacts manufacturing operations, enabling deeper insights into production processes. By analyzing production data, companies identify inefficiencies, streamline workflows, and enhance operational efficiency and productivity. Predictive maintenance through sensor data analysis prevents machine failures, while logistics data analysis optimizes supply chains and inventory management, reducing costs and enhancing competitiveness. However, implementing Big Data analytics presents challenges such as rapid data growth, diverse data sources, real-time insights, skill shortages, and data fragmentation. Overcoming these hurdles requires robust technology, skilled personnel, and effective data management strategies. Examples of Big Data analytics applications include customer behavior analysis by Amazon and Netflix, fraud detection in insurance, and urban mobility optimization. Success factors in data analytics implementation include effective data-driven communication, technology integration, and skill enhancement. In conclusion, implementing Big Data Analytics in manufacturing promises significant benefits in operational efficiency, product quality, and competitiveness. Overcoming challenges necessitates robust strategies and consideration of ethical and security issues, ensuring responsible data usage. With a deep understanding of Big Data Analytics, manufacturing companies can leverage this technology to achieve higher efficiency and competitiveness in the global market.

Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments

The application of big data analytics in satellite network management has emerged as a transformative approach to optimize performance and enhance reliability in the satellite telecommunications industry. This paper reviews the current state of big data analytics in satellite network management, highlighting its key applications and benefits. By analyzing large volumes of data generated by satellite networks, big data analytics enables satellite telecommunications companies to gain valuable insights into network performance, identify potential issues, and take proactive measures to ensure optimal performance. One of the key applications of big data analytics in satellite network management is predictive maintenance. By analyzing historical data and equipment performance metrics, companies can predict when equipment is likely to fail and take preventive measures to avoid downtime. This not only improves network reliability but also reduces maintenance costs and improves overall operational efficiency. Another important application is network optimization. Big data analytics can analyze network traffic, weather conditions, and other factors to optimize satellite beam coverage, frequency allocation, and routing. This helps companies maximize bandwidth utilization, reduce interference, and improve service quality. The implications of big data analytics for future technology developments in satellite network management are significant. As the volume of data generated by satellite networks continues to grow, there is a need for advanced analytics tools and techniques to process and analyze this data efficiently. Future technology developments in areas such as AI, machine learning, and data visualization are expected to play a key role in enhancing the capabilities of big data analytics in satellite network management. In conclusion, the application of big data analytics in satellite network management offers significant benefits in terms of optimizing performance and enhancing reliability. By leveraging the insights provided by big data analytics, satellite telecommunications companies can improve operational efficiency, reduce costs, and deliver better services to their customers. Future technology developments will further enhance the capabilities of big data analytics, paving the way for more efficient and reliable satellite network management.

Application of Big Data Analysis of Traffic Accidents and Violation Reports for Improving Traffic Safety

Application of Big Data Analysis of Traffic Accidents and Violation Reports for Improving Traffic Safety

Application of big data analysis in water pollution monitoring

Contemporarily, the quick development of an abundant amount of big data analysis technologies has brought great convenience to individuals' everyday existence. In terms of environmental protection, especially water pollution monitoring, this technological progress is particularly critical. As the global demand for clean water resources grows and global industrialization intensifies pollution of the water environment, the adoption of advanced data analysis technologies has become critical. Among the vast array of machine learning architectures, three particularly stand out due to their significance and widespread adoption: the artificial neural network (ANN), which serves as a foundational pillar in understanding complex data patterns; the multi-layer perceptron neural network (MLPNN), a sophisticated evolution that allows for deeper computations and learning; and the adaptive neuro-fuzzy inference system (ANFIS), which brilliantly combines neural and fuzzy logic principles for intricate problem solving. These models not only have high accuracy due to their wide application, but they still have their own limitations. This article aims to introduce the methods, basic principles, and application scenarios of these models. In addition, this article also compares the advantages and limitations of these machine learning models, thereby providing some new ideas for future improvements and innovations in model algorithms, application scenarios, and integration.

Investigation of Artificial Intelligence Algorithms in Robot Object Recognition Systems Under the Background of Big Data

In the long history of human beings, with the continuous exploration and research of natural phenomena and social life, many scientific fields have emerged, and robots are the product of this technological development to a certain stage. At present, there are hundreds of different types of robots applied in production and daily life in the world, which have achieved significant economic benefits. However, its technical issues have gradually emerged. For example, the shortcomings in visual perception and other aspects cannot be effectively addressed. Object recognition is not precise enough, and information resources cannot be effectively utilized to achieve control functions. These are the main factors that constrain the further progress and improvement of robots. The emergence of big data and Artificial Intelligence (AI) has brought unprecedented opportunities to robots. Especially, the application of big data analysis in intelligent manufacturing and smart city construction is becoming increasingly widespread, thus providing new solutions for robot services. They not only enable people to quickly and accurately grasp a large amount of valuable knowledge, but also better tap into the enormous potential contained in human intelligence, which largely drives the robot industry towards intelligence. By summarizing the existing research results, this paper explored the development trend of robot object recognition systems, and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement. In response to the current problems, corresponding solutions were proposed and comparative experiments were designed. This proved that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.

A study on big data analytics and innovation: From technological and business cycle perspectives

In today's rapidly changing business landscape, organizations increasingly invest in different technologies to enhance their innovation capabilities. Among the technological investment, a notable development is the applications of big data analytics (BDA), which plays a pivotal role in supporting firms' decision-making processes. Big data technologies are important factors that could help both exploratory and exploitative innovation, which could affect the efforts to combat climate change and ease the shift to green energy. However, studies that comprehensively examine BDA's impact on innovation capability and technological cycle remain scarce. This study therefore investigates the impact of BDA on innovation capability, technological cycle, and firm performance. It develops a conceptual model, validated using CB-SEM, through responses from 356 firms. It is found that both innovation capability and firm performance are significantly influenced by big data technology. This study highlights that BDA helps to address the pressing challenges of climate change mitigation and the transition to cleaner and more sustainable energy sources. However, our results are based on managerial perceptions in a single country. To enhance generalizability, future studies could employ a more objective approach and explore different contexts. Multidimensional constructs, moderating factors, and rival models could also be considered in future studies.

Big Data Analytics: Techniques, Tools, and Applications in Various Industries


 
 
 Big data analytics has emerged as a powerful tool for extracting valuable insights from large and complex datasets across diverse industries. This article provides a comprehensive review of the techniques, tools, and applications of big data analytics in various industries. Through qualitative analysis and extensive literature review, this study explores the key methods and technologies used in processing and analyzing massive volumes of data to uncover hidden patterns, trends, and correlations. The article discusses the significance of big data analytics in enhancing decision-making processes, optimizing operations, and gaining competitive advantages in today's data-driven economy. Furthermore, it examines the challenges and opportunities associated with implementing big data analytics solutions across different sectors. By examining real-world case studies and industry examples, this research highlights the practical applications of big data analytics in areas such as healthcare, finance, manufacturing, retail, and marketing. The findings underscore the transformative impact of big data analytics on improving business performance, customer engagement, and innovation across diverse domains. Overall, this article aims to contribute to the understanding of big data analytics techniques and their wide-ranging applications, providing insights for researchers, practitioners, and decision-makers in leveraging data-driven approaches to drive organizational success.
 
 

Exploring the dynamics of bigdata adoption in the Indian food industry with fuzzy analytical hierarchical process

PurposeThe main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.Design/methodology/approachThe study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.FindingsThe findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.Research limitations/implicationsThis research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.Originality/valueThe present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.

Multimodal Imaging in Oncology: Challenges and Future Directions

Multimodal imaging, a vital tool in oncology, combines various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to offer valuable insights for tumor detection, diagnosis, staging, and treatment planning. Despite its advantages, challenges persist in image registration, fusion, optimization of algorithms, hardware advancements, and the integration of artificial intelligence (AI) and computer vision techniques. This paper delves into the current state of multimodal imaging in oncology, addresses the challenges faced, explores potential solutions, and underscores future developments to augment diagnostic and therapeutic capacities, including the application of AI, computer vision, and big data analytics to enhance the efficiency and effectiveness of multimodal imaging in cancer diagnosis and treatment. Additionally, it highlights how AI-powered algorithms can automate image analysis, assist in tumor segmentation, and provide predictive insights, ultimately improving the precision and speed of diagnosis and treatment planning in oncology.

Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability, customer demand forecasting, cheaper development of products, and improved stock control. Small and medium sized enterprises (SMEs) are the backbone of the global economy, comprising of 90 % of businesses worldwide. However, only 10 % SMEs have adopted big data analytics despite the competitive advantage they could achieve. Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics. The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK. This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners. The results of the evaluation are presented with a discussion on the results, and the paper concludes with recommendations to improve the scoring tool based on the proposed framework. The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.