Big Data Applications Research Articles

"I don't wanna die, but my brain insists that I should": a big qualitative data approach to the lived experiences of suicidal thoughts.

There remains a dearth of knowledge concerning the phenomenology of suicidal thoughts, with research focusing on reasons for feeling suicidal rather than their mental expression. While clinical interviews remain the standard phenomenological approach, such exploration of lived experiences may prove challenging for this sensitive topic. As a complementary alternative, the use of naturally-occurring online data is opportune for capturing elaborations on tabooed phenomena. In this phenomenological study, we present a thematic analysis on lived experiences of suicidal thoughts as spontaneously reported by non-identified users of a Reddit online board (r/Depression), collecting 668 posts using the search terms "suicidal ideation," "suicidal thoughts," and "suicide." Codes were grouped into descriptive categories summarizing the properties of thoughts, their effects, and their relation to suicide. Then, an interpretative synthesis yielded global themes connecting salient meanings on the experience of suicidal thoughts. With a long-term and recurring nature, thoughts of suicide appear in the form of vivid imagery and daydreaming's, initially bringing relief to adverse feelings but eventually becoming conditioned and all-consuming. Rather than a wonderment, they are experienced as intrusive thoughts by people struggling to make meaning of their occurrence. When conciliating the presence of unwanted thoughts, users express intricate relations to wishing or not to die, as well as varying perceptions of control over actions or fear of suicidal behavior. With an innovative application of big qualitative data into phenomenological analysis, this study contributes to an initial characterization of suicidal thoughts, uncovering findings that are not contemplated into current conceptualizations of suicidality. The analysis is limited by a restricted context of posts and unknown demographics, and further research with clinical interviews is warranted for in-depth exploration of suicidal thoughts.

Research on Site Selection Planning of Urban Parks Based on POI and Machine Learning—Taking Guangzhou City as an Example

Against the background of smart city construction and the increasing application of big data in the field of planning, a method is proposed to effectively improve the objectivity, scientificity, and global nature of urban park siting, taking Guangzhou and its current urban park layout as an example. The proposed approach entails integrating POI data and innovatively applying machine learning algorithms to construct a decision tree model to make predictions for urban park siting. The results show that (1) the current layout of urban parks in Guangzhou is significantly imbalanced and has blind zones, and with an expansion of the search radius, the distribution becomes more concentrated; high-density areas decrease from the center outward in a circle, which manifests as a pattern of high aggregation at the core and low dispersion at the edge. (2) Urban park areas with a service pressure of level 3 have the largest coverage and should be prioritized for construction as much as possible; there are fewer areas at levels 4 and 5, which are mainly concentrated in the central city, and unreasonable resource allocation is a problem that needs to be solved urgently. (3) There was a preliminary prediction of 6825 sites suitable for planning, and the fit with existing city parks was 93.7%. The prediction results were reasonable, and the method was feasible. After further screening through the coupling and superposition of the service pressure and the layout status quo, 1537 locations for priority planning were finally obtained. (4) Using the ID3 machine learning algorithm to predict urban park sites is conducive to the development of an overall optimal layout, and subjectivity in site selection can be avoided, providing a methodological reference for the planning and construction of other infrastructure or the optimization of layouts.

Using Data Mining Principles in Implementing Predictive Analytics to Different Areas

This study delves into the realm of information-based knowledge discovery technologies and underscores the growing necessity for extensive data representation to enhance the management of care and mitigate the financial costs associated with promoting long-term care. The proliferation of information collected and disseminated through the Internet has reached unprecedented levels in the context of long-term financial health statistics, posing a challenge for businesses to effectively leverage this wealth of data for research purposes. The explicit specification of costs becomes paramount when dealing with substantial volumes of data. Consequently, the literature on the application of big data in logistics is categorized based on the nature of methods employed, such as explanatory, predictive, regulatory, strategic, and operational approaches. This includes a comprehensive examination of how big data analysis is applied within large corporations. In the healthcare domain, the study contributes to the evaluation of usability by providing a framework to analyze the maturity of structures at four distinct levels. The emphasis is particularly on the pivotal role played by predictive analytics in the healthcare industry through big data methodologies. Furthermore, the study advocates for a paradigm shift in management's perception of large business data sets, urging them to view these as strategic resources that must be seamlessly integrated into the company. This integration is seen as imperative for achieving comprehensive business analysis and staying competitive in the ever-evolving landscape of healthcare. The study concludes by shedding light on the limitations inherent in the research and delineating the specific focus areas that have been addressed.

Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science.

Maximum entropy (MaxEnt) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, MaxEnt models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for MaxEnt models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical MaxEnt models lack. In this paper, we present novel optimization algorithms that overcome the shortcomings of state-of-the-art algorithms for training large-scale, non-smooth MaxEnt models. Our proposed first-order algorithms leverage the Kullback-Leibler divergence to train large-scale and non-smooth MaxEnt models efficiently. For MaxEnt models with discrete probability distribution of n elements built from samples, each containing m features, the stepsize parameter estimation and iterations in our algorithms scale on the order of O(mn) operations and can be trivially parallelized. Moreover, the strong ℓ1 convexity of the Kullback-Leibler divergence allows for larger stepsize parameters, thereby speeding up the convergence rate of our algorithms. To illustrate the efficiency of our novel algorithms, we consider the problem of estimating probabilities of fire occurrences as a function of ecological features in the Western US MTBS-Interagency wildfire data set. Our numerical results show that our algorithms outperform the state of the art by one order of magnitude and yield results that agree with physical models of wildfire occurrence and previous statistical analyses of wildfire drivers.

Research on the Application of Big Data and Cloud Computing Technology in Smart Campus

The current education field is experiencing an innovation driven by big data and cloud technologies, and these advanced technologies play a central role in the construction of smart campuses. Big data technology has a wide range of applications in student learning behavior analysis, teaching resource management, campus safety monitoring, and decision support, which improves the quality of education and management efficiency. Cloud computing technology supports the integration, distribution, and optimal use of educational resources through cloud resource sharing, virtual classrooms, intelligent campus management systems, and Infrastructure-as-a-Service (IaaS) models, which reduce costs and increase flexibility. This paper comprehensively discusses the practical application of big data and cloud computing technologies in smart campuses, showing how these technologies can contribute to the development of smart campuses, and laying the foundation for the future innovation of education models.

The Application of Big Data and Fuzzy Decision Support Systems in the Innovation of Personalized Music Teaching in Universities

Personalized music teaching in universities improves students’ learning and efficiency through adaptive guidance. This adaptability requires large study data and intelligent decisions based on the learner’s ability. This article introduces a Definitive Teaching Support System (DTSS) exclusive to music learning to augment this concept. This system is designed to increase the adaptability of music learning based on student interest and ability. The system is powered by a fuzzy decision system for identifying maximum teaching adaptability to personalized processes. Low-to-high-sorted personalization provides new endorsements for further music sessions in the fuzzy derivative process. Maximum adaptability is the target for new personalized sessions in the universities. This differs for various students from which a common adaptability level for monotonous recommendations is identified. The identified adaptability is set as a global maximum solution towards music learning personalization. The defuzzification reduces the chances of low adaptability by expelling the stationary adaptability outcomes.

Big Data and Privacy with a focus on Statistical Approaches to Ensuring Data Confidentiality

In the era of Big Data, where vast datasets fuel innovation across industries, the paramount concern remains safeguarding individual privacy. This article explores how statistical approaches ensure data confidentiality amidst the proliferation of digital information. Differential privacy techniques introduce calibrated noise to protect identities while preserving data utility, crucial for compliance and trust in data-driven decision-making. Secure Multiparty Computation (MPC) enables collaborative analysis without exposing raw data, supporting privacy in sectors like healthcare and finance. Privacy-preserving data mining techniques integrate encryption and anonymization to extract insights while shielding sensitive information. Anonymization and de-identification methods further bolster privacy by masking identifiable data, essential for adhering to stringent regulations like GDPR and HIPAA. As data generation escalates, advancing these statistical methods is essential for maintaining privacy integrity in the evolving landscape of Big Data applications. Keywords: Big Data, Privacy, Statistical approaches, Differential privacy, Secure multiparty computation.

Solving supply chain management issues with AI and Big Data analytics for future operational efficiency

This review paper examines the use of artificial intelligence (AI) and big data analytics in solving supply chain management (SCM) issues and enhancing future operational efficiency. The primary objective is to synthesize existing research and provide a comprehensive overview of how these technologies are revolutionizing SCM. The paper systematically reviews recent literature on AI and big data applications in SCM, focusing on key areas such as demand forecasting, inventory management, and logistics optimization. By analyzing various studies and case examples, it highlights the transformative effects of these technologies on supply chain processes. The review covers a range of AI techniques including machine learning, deep learning, and predictive analytics, as well as the role of big data in capturing and processing large volumes of supply chain-related information. Key findings from the literature indicate significant improvements in supply chain visibility, decision-making accuracy, and operational efficiency. AI and big data analytics enable more precise demand forecasting, better inventory control, and optimized logistics, leading to cost reductions and enhanced responsiveness to market fluctuations. The review also discusses the challenges and considerations for implementing these technologies, such as data quality, integration complexity, and the need for specialized skills. The paper emphasizes the critical role of AI and big data analytics in addressing contemporary SCM issues and fostering future-ready supply chains. It underscores the necessity for organizations to adopt these technologies to stay competitive and achieve long-term operational excellence. The insights provided serve as a valuable resource for researchers and practitioners aiming to leverage AI and big data for supply chain innovation. Keywords: Artificial Intelligence (AI), Big Data Analytics (BDA), Supply Chain Management (SCM), Predictive Analytics, Internet of Things (IoT), Blockchain Technology, Automation, Machine Learning, Inventory Management, Logistics Optimization, Supply Chain Visibility, Real-time Monitoring, Data Quality, System Integration, Risk Management, Operational Efficiency, Strategic Decision-making, Sustainability, Supply Chain Resilience, Emerging Technologies.

Big data development and agricultural carbon emissions: Exacerbation or suppression? A quasi-natural experiment based on the establishment of the National Big Data Comprehensive Pilot Zone

As a strategic resource, big data has become a key force affecting carbon emission reduction in agriculture. However, its impacts remain controversial, and relevant empirical evidence remains to be explored. Based on quasi-natural experimental analysis, this study explored the impact and mechanism of the construction of the National Big Data Comprehensive Pilot Zone (NBDCPZ) on agricultural carbon emissions (ACE) in China and adopted a difference-in-difference (DID) model using China's provincial panel data from 2003 to 2020. The results showed that the ACE in the NBDCPZ establishment area was significantly reduced by 11.91%, a finding that remained robust following the parallel trend test and the placebo test, among others. Mechanism analysis showed that the ACE was reduced through industrial upgrading and technological innovation. Heterogeneity analysis showed that more pronounced policy gains were achieved in China's central-eastern regions as well as in non-major grain-producing areas compared to western and major grain-producing areas. This research provided supporting evidence for the prospect of big data application in ACE and provided useful guidance regarding the promotion of green and sustainable agricultural development.

Developing a system for the real-time collection and analysis of mobile vehicle emission data

In this study, an integrated system for remotely monitoring air pollution data was developed. This system combines vehicle exhaust emission data with driving information. It comprises an exhaust extraction device (EED), an improved On-Board Diagnostic II (OBDII) device, a mobile app, and a backend server database. The EED detects exhaust gases such as NOx, COx, and PM2.5 and transmits this information to the OBDII device through 2.4G communication. The OBDII device gathers vehicle driving information through a CAN 2.0 interface, integrates this information with vehicle exhaust emission data, and transmits the integrated information to the app through Bluetooth. The app forwards the integrated data to the backend database through 4G/5G communication for storage. Overall, this system provides a platform for the comprehensive collection, analysis, and application of big data pertaining to vehicle pollution sources and driving behavior. Analysis of actual driving experiments involving three vehicles revealed a correlation of over 80 % between vehicles’ revolutions per minute and CO2 and PM2.5 emissions, indicating that driving behaviors affect vehicle exhaust emissions. In summary, the proposed system not only stores relevant driving information in real time but also provides a platform for the future analysis of driving behavior and vehicle exhaust emissions.

Digital Policy, Green Innovation, and Digital-Intelligent Transformation of Companies

In the midst of rigorous market rivalry, enhancing a company’s competitiveness and operational efficiency in an era of rapid IT advancement is a pressing concern for business leaders. The National Big Data Comprehensive Zone (BDCZ) pilot scheme, instituted by the Chinese government, systematically addresses seven core objectives, encompassing data resource management, sharing and disclosure, data center consolidation, application of data resources, and the circulation of data elements. This policy initiative aims to bolster the establishment of information infrastructure through big data applications, facilitate the influx and movement of talent, and propel corporate sustainable growth. Utilizing a quasi-natural experiment approach, we assess the pilot policy’s influence on the digital-intelligent transformation (DIT) of manufacturing companies from a green innovation ecosystem perspective, employing datasets from 2010 to 2022, and methodologies such as Difference-in-Differences (DID), Synthetic Differences-in-Differences (SDID), and Propensity Score Matching-DID (PSM-DID). The findings indicate that the BDCZ initiative significantly fosters DIT in manufacturing companies. The policy’s establishment confers benefits, including access to increased government support and innovation capital, thereby enhancing the sustainability of green innovation efforts. It also strengthens corporate collaboration, engendering synergistic benefits that improve regional economic progression and establish a conducive environment for digital development, ultimately enhancing the regional innovation ecosystem. The pilot policy’s impact varies across entities, with more profound effects observed in developed financial markets compared to underdeveloped ones. Additionally, non-state-owned companies exhibit a greater response to BDCZ policy interventions than their state-owned counterparts. Moreover, manufacturing bussiness with a higher proportion of executive shareholding are more substantially influenced by the BDCZ. This article fills the research gap by using the quasi-natural experiment of BDCZ to test the impact on DIT of companies and provides inspiration for local governments to mobilize the enthusiasm of manufacturing companies for DIT.

Promoting the Application of Big Data in Construction through Stakeholder Collaboration Based on a Two-Mode Network

Presently, the application of big data in the construction industry encounters numerous obstacles and involves diverse stakeholders, with the intricate network of relationships between these factors and stakeholders remaining unclear. Investigating stakeholders’ management priorities and collaborative patterns can facilitate the development of BDAC. Therefore, this study employs a two-mode social network analysis to explore stakeholders’ power and attitudes toward the factors of BDAC. Firstly, the initial list of stakeholders and factors is identified based on the literature and expert interviews, followed by a questionnaire to establish stakeholder–factor relationships and construct the network. Subsequently, the adjacency matrix, centrality, core–periphery structure, and hierarchical cluster are adopted to analyze the network. The results found that (1) technical factors need to be addressed by all stakeholders due to complexity; (2) due to the low resource similarity of factors and low power similarity of stakeholders, all stakeholders should be involved in the collaboration; and (3) government, developers, and consultants, as core stakeholders, exhibit a proactive inclination towards collaborative efforts in addressing central factors, and can coordinate with peripheral stakeholders. Consequently, this study establishes a stakeholder collaboration model centered on the government–developer–consultant trio, which provides clear responsibility allocation and strategic guidance for fostering long-term, effective collaboration in BDAC.

The Application of Big Data in the Management of Ideological and Political Education in the Development of Education Network

Based on a brief analysis of the current situation of university education management and research on intelligent algorithms, this article constructs a university education management system based on big data. For the clustering and prediction modules in higher education management, corresponding algorithms are used for optimization design. A fuzzy clustering algorithm based on entropy weight is proposed to address the shortcomings of the C-means clustering algorithm. This algorithm adds weighting coefficients on the basis of improvement and continuously updates the clustering centers. The prediction module uses Apriori algorithm to map and compress the target transaction database, reduces the number of candidate item sets through pruning process, and designs simulation experiments to measure the performance of the algorithm. The simulation results show that the clustering results of this algorithm are closer to the actual clustering situation, with shorter running time and better algorithm performance.

Research on Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities Under Big Data Environment

In innovation and entrepreneurship education, how to effectively use big data technology to improve the quality of education has become an urgent problem. In this article, the authors summarize the development of big data technology and its application background in higher education and point out the limitations of traditional education quality evaluation methods. They discuss the research status of the quality evaluation of innovation and entrepreneurship education in colleges and universities at home and abroad and the application of big data in education quality evaluation. Using the fuzzy comprehensive evaluation method, they constructed a quality evaluation model of innovation and entrepreneurship education in colleges and universities based on big data and proposed a corresponding evaluation process and index system. Finally, through empirical analysis, they verified the effectiveness and operability of the evaluation model. This research has theoretical and practical significance for improving the quality of innovation and entrepreneurship education in colleges and universities.

SpectralGPT: Spectral Remote Sensing Foundation Model.

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS Big Data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; and 4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS Big Data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.

Big Data in online education

From classifying learners to predicting learner behaviour, the application of Big Data in online education has been vast. Besides the potential benefits of Big Data in education, it is necessary to critically engage with some ethical and social challenges that Big Data presents to the field of online learning. The increasing use of big data by large institutional actors and corporations raises questions not only about data privacy and ownership, but whether this data is used to genuinely improve learner and teacher online learning experiences, or primarily for commercial profits and institutional benefits. When addressing ethical concerns regarding the use of Big Data in education, critiques often follow a reasoning that is in line with corporate interests and neoliberal logic of marketization of education. Given the importance of the pursuit for democratic online education, the need for critical perspectives in the field is ever-more essential. This research tries to critically address the role and impact of Big Data on labour relations and economic fairness in online education by examining both corporate and institutional data practices in online learning. The study puts forward a provisional theory of the use of Big Data in two large online learning platforms (Coursera and Blackboard) using critical grounded theory. The core category of Exploitation of the learning community, the three constituent concepts; the Vendor-Institutional Complex, Use of learner generated value for profit, and the Behavioral monitoring and engineering; and the sustaining category, the Magic Trick, were the foundational blocks for developing an emancipatory theory that addressed ethical issues of economic fairness regarding the use of big data in online education.

Big data for epidemic preparedness in southeast Asia: An integrative study

This integrative study explores the pivotal role of big data in enhancing epidemic preparedness and response strategies in Southeast Asia. The region, characterized by its diverse socio-economic landscape and frequent exposure to infectious disease threats, necessitates innovative approaches to mitigate the impact of epidemics. Big data, with its vast and varied sources, offers unprecedented opportunities to revolutionize epidemic surveillance, prediction, and management. Drawing on a comprehensive review of literature and case studies, this paper delves into the multifaceted applications of big data in epidemic preparedness. It examines the utilization of diverse data sources, including social media, mobile phones, and IoT devices, for real-time monitoring of disease outbreaks and trends. Moreover, it elucidates the role of advanced analytics techniques, such as machine learning and data visualization, in informing timely and targeted response strategies. Furthermore, this study underscores the importance of collaborative efforts and partnerships in leveraging big data for epidemic preparedness. It explores successful initiatives that have demonstrated the effectiveness of data sharing and interdisciplinary collaboration in enhancing epidemic response capabilities across borders. However, the integration of big data into epidemic preparedness initiatives presents various challenges, including data quality issues, privacy concerns, and the need for robust infrastructure and expertise. Addressing these challenges requires concerted efforts from governments, healthcare organizations, and the private sector. In conclusion, this study advocates for continued research, investment, and policy support to harness the full potential of big data in epidemic preparedness. By embracing innovative technologies and fostering collaboration, Southeast Asia can build resilient health systems capable of effectively combating emerging infectious disease threats.

‘This is NOT human services’: Counter-mapping automated decision-making in social services in Australia

This paper offers a counter-map of automation in social services decision-making in Australia. It aims to amplify alternative discourses that are often obscured by power inequalities and disadvantage. Redden (2005) has used counter-mapping to frame an analysis of big data in government in Canada, contrasting with ‘dominant outward facing government discourses about big data applications’ to focus on how data practices are both socially shaped and shaping. This paper reports on a counter-mapping project undertaken in Australia using a mixed methods approach incorporating document analysis, interviews and web scraping to amplify divergent discourses about automated decision-making. It demonstrates that when the focus of analysis moves beyond dominant discourses of neoliberal efficiency, cost cutting, accuracy and industriousness, alternative discourses of service users’ experiences of automated decision-making as oppressive, harmful, punitive and inhuman(e) can be located.

Application-based big data development framework for health sciences libraries.

The study is a part of a student research project on performance-based evaluation of big data application in health sciences libraries. It presents a theoretical framework of the performance-based evaluation system for health institutes' libraries in the digital environment. The health sciences libraries' systematic approach was judged based on the five main components (data culture, organisational infrastructure, responsibilities, skills and technology competence) of big data analytics (BDA). A comprehensive literature review of the published studies was undertaken related to BDA, including the diffusion of innovation theory, and the theoretical background of the technology acceptance model to produce an application-based big data development framework for the health sciences libraries. The application-based evaluation model integrates BDA in health sciences libraries for improving library services and performance. The study proposed a need for skilled professionals with the knowledge and experience both professionally and technically. Finally, the study proposed a model that will help to measure the organisation's ability to analyse vast amounts of data to empirically validate the association concerning big data analysis and analytical practices in health libraries.

AI and big data in economic regulation: A comparative analysis of China and the United States

This paper examines the application of artificial intelligence (AI) and big data in economic regulation within China and the United States, highlighting the differing approaches and outcomes. In China, the centralized governance structure allows for the swift and uniform implementation of AI-driven strategies, optimizing government strategies, and balancing economic growth with social equity. The National Development and Reform Commission (NDRC) and the People's Bank of China (PBOC) are key players in utilizing AI to forecast economic trends and stabilize the economy. Conversely, the U.S. employs a decentralized approach, with AI applications driven primarily by the private sector and academia. The Federal Reserve leverages AI for policy decisions, while private firms use predictive models to enhance market strategies. Big data analysis supports decision-making in both nations, but differing governance structures lead to unique challenges and benefits. This study compares the centralized and decentralized systems, assessing their impact on economic performance and policy flexibility. The findings provide insights into how AI and big data can be optimized for economic regulation, offering lessons for other countries in adopting these technologies.