Big Data Research Articles

Talent scouting and standardizing fitness data in football club: systematic review

Talent scouting and fitness data standardization in professional football clubs have been the focus of research in recent years. The role of technology, big data, and data analytics is crucial in reshaping sports performance and bolstering the competitiveness of professional football teams. This review aims to consolidate current research on these technological advancements, examining how they can be effectively harnessed to revolutionize talent sourcing and elevate the competitive standards within football clubs. The review will focus on participants, including professional football players, and intervention interventions such as the implementation of digital technologies, data mining, and machine learning techniques. The study designs will include experimental, observational, and mixed-method studies conducted in football club settings. The analysis highlights the transformative potential of integrating quantitative player statistics with advanced data science and data-driven approaches in revolutionizing sports performance, enabling clubs to make informed recruitment decisions and enhance team performance. In addition, the review focuses on the impact of data analytics on transforming sports performance. The research has demonstrated that these models, specifically classification and regression models, can forecast a football player's performance score with up to 94% accuracy for forward positions.

The Role of Digital Technologies in Climate Change Management: Strategies for Minimizing the Environmental Impact of USA Companies

The article examines the role of digital technologies in climate change management and reducing the environmental impact of U.S. companies. Innovations such as artificial intelligence, the Internet of Things, and big data are analyzed for their ability to help companies minimize CO2 emissions, optimize resource use, and improve energy efficiency. Challenges companies face when implementing digital solutions: high initial costs, integration difficulties, and regulatory complexities, are discussed. Strategies for overcoming these barriers to achieve sustainable development are proposed.

Big Data in the Oil & Gas Industry

Big Data in the Oil & Gas Industry

Inferring Surface NO2 Over Western Europe: A Machine Learning Approach With Uncertainty Quantification

AbstractNitrogen oxides (NOx = NO + NO2) are of great concern due to their impact on human health and the environment. In recent years, machine learning (ML) techniques have been widely used for surface NO2 estimation with rapid developments in computational power and big data. However, the uncertainties inherent to such retrievals are rarely studied. In this study, a novel ML framework has been developed, enhanced with uncertainty quantification techniques, to estimate surface NO2 and provide corresponding data‐induced uncertainty. We apply the Boosting Ensemble Conformal Quantile Estimator (BEnCQE) model to infer surface NO2 concentrations over Western Europe at the daily scale and 1 km spatial resolution from May 2018 to December 2021. High NO2 mainly appears in urban areas, industrial areas, and roads. The space‐based cross‐validation shows that our model achieves accurate point estimates (r = 0.8, R2 = 0.64, root mean square error = 8.08 μg/m3) and reliable prediction intervals (coverage probability, PI‐50%: 51.0%, PI‐90%: 90.5%). Also, the model result agrees with the Copernicus Atmosphere Monitoring Service (CAMS) model. The quantile regression in our model enables us to understand the importance of predictors for different NO2 level estimations. Additionally, the uncertainty information reveals the extra potential exceedance of the World Health Organization (WHO) 2021 limit in some locations, which is undetectable by only point estimates. Meanwhile, the uncertainty quantification allows assessment of the model's robustness outside existing in‐situ station measurements. It reveals challenges of NO2 estimation over urban and mountainous areas where NO2 is highly variable and heterogeneously distributed.

Hierarchical Holographic Modeling of Network Ideology Risks

The rapid evolution of computer network technology and the proliferation of social information have elevated the role of computer networks in various domains. However, this progress has also introduced new risks to network ideology security within the big data environment. Using a hierarchical holographic modeling method, this study identifies three key risks—subject-related, information-related, and environmental risks—and constructs a comprehensive three-level index framework for evaluating network ideology risks. By integrating network social governance with the complexities of the big data era, the study proposes an optimized pathway for network social governance, enriching traditional research approaches. The introduction of the LR-NSRPM method reduces CPU and RAM utilization by 21.65% compared to existing methodologies. Additionally, the study outlines observable risk indicators and high-risk elements within network ideology, providing valuable insights for managing network ideology risks in the big data environment.

Efficient Credit Card Fraud Detection System Using Big Data and Machine Learning

This review offers a detailed strategy to address the growing threat of credit card fraud in today's digital landscape. By utilizing Big Data analytics alongside machine learning methods, the system aims to transform fraud detection processes. It tackles the challenges arising from the increasing volume and complexity of credit card transactions, enabling the real-time detection and prevention of fraudulent actions. The system employs sophisticated machine learning algorithms to identify patterns and anomalies linked to fraudulent activities, allowing for proactive responses to emerging fraud tactics. Additionally, the system is optimized to handle and analyze large datasets efficiently, ensuring timely and precise detection of fraud. It also incorporates strong security protocols to protect sensitive customer data while adhering to privacy regulations. This review ultimately seeks to enhance the safety and reliability of electronic payments, protecting financial institutions and consumers from the harmful effects of credit card fraud.

Differences Between Serum and Plasma: An Indirect Approach to Derive a Combined Reference Interval for Total Protein, Albumin, and Globulin

Abstract The objective of our study is to derive reference intervals (RI) for total protein (TP), albumin and globulin that are appropriate for both plasma and serum samples by applying statistical analyses to retrospective patient data. Both plasma and serum are acceptable matrices for these tests at our institution, with a single RI that applies to all specimens. The widespread shortage of lithium heparin (LH) tubes that occurred as a consequence of the pandemic prompted our lab to switch the primary tube type for comprehensive metabolic panels (CMPs) from LH to serum separator tubes (SST) in October 2021. The primary tube type reverted to LH in October 2023, and no adjustments were made to RIs at either time. To determine how well our RIs align with the accepted matrices, we used R Statistical Programing version 4.3.1 to apply an indirect, nonparametric statistical approach to data extracted from the laboratory information system (LIS) between July 2018 and December 2023. All orders for TP, albumin and globulin were queried to include the following components in the analysis: TP, albumin, globulin, serum TP, serum albumin and serum globulin. The records were divided into 3 epochs based on the primary tube type for CMP on the order date, with epochs 1 and 3 reflecting LH and epoch 2 reflecting SST. We attempted to identify a subset of serum and plasma samples in healthy individuals by applying exclusion criteria based on inpatient status, age, and draw station at which the specimen was collected. For each component, a resampling bootstrap method was used to calculate the RIs and corresponding 95% confidence intervals by epoch. Our results show that the RI of TP, albumin and globulin changed significantly between the epochs, reflecting the difference in primary matrix. The components serum TP, serum albumin, and serum globulin did not see a significant change in values over the epochs. A verification study looking at plasma and serum samples in 20 healthy individuals support these results. Based on these findings, our reference intervals were updated from 6.6-8.7 g/dL to 5.9-8.3 g/dL for TP, from 3.6-4.9 g/dL to 3.6-5.1 g/dL for albumin and from 2.3-3.5 g/dL to 1.5-3.9 g/dL for globulin. We show that there are statistically and potentially clinically significant differences in the reference intervals of TP, albumin and globulin in serum compared to plasma. This study demonstrates a big data analytics approach to derivation of RIs and emphasizes the importance of RI assessment and adjustment for patient population and acceptable specimen type.

What does e-invoice data bring to SNA and real-time economy?

This paper explores the potential of electronic invoice data to enhance the System of National Accounts (SNA) and facilitate a Real-Time Economy (RTE). The rise of data science and advancements in information and communication technologies have increased the use of big data in economic statistics. E-invoice data is being digitized in Europe to detect VAT fraud, but it also contains a vast amount of information on economic entities that make up the SNA. This study focuses on integrating electronic accounting information to improve the coverage, granularity, and update speed of SNA. The paper examines the concept of RTE, which emphasizes real-time data processing and automation, particularly in Estonia and Finland. It also highlights the benefits and challenges of using invoice data for economic statistics, including issues related to data storage formats, legislative systems, and the coordination of data linkages. The research demonstrates invoice data’s theoretical and practical applications for constructing Input–Output Tables (IOTs) and monitoring financial situations. Despite the technical and institutional hurdles, the study suggests integrating invoice data can significantly transform economic statistics.

Sequential bias-corrected weighted least squares solution of mixed additive and multiplicative error models

In the era of big data, the number of observations in adjustment calculations may reach tens or even hundreds of thousands. When dealing with these large dataset problems, many matrix operations are often required. At this time, the dimensions of the matrix will be large, which will generate a great computational burden. At present, no research results have been published on the computational efficiency of bias-corrected weighted least squares (bcWLS) for mixed additive and multiplicative error models (MAMEM). Sequential adjustment (SEA) groups the observations for calculation and can provide the same computational precision while greatly improving computational efficiency. This paper applies the idea of SEA to the calculation of bcWLS and proposes an iterative solution for sequential bcWLS (SEbcWLS). Using three simulation experiments to verify the effectiveness of our method, it was found that when the number of observations is 10000, the effect is better when the number of groups does not exceed 100, achieving the same precision as the original method while having high computational efficiency. The calculation results of line fitting and plane fitting are not affected by the number of grouping groups. For DEM (Digital elevation model) experiments with strong nonlinearity, when the number of grouping groups is too large, the effect is not very good, but the calculation efficiency is also higher than the original method, and the difference in calculation results is not significant.

Investigating the determinants of homestay satisfaction on Airbnb using multiple techniques

Peer-to-peer accommodation has gained prominence in the sharing economy and e-commerce sectors, with big data playing a crucial role in understanding customer preferences and evaluating homestay satisfaction. This study proposes a novel methodology that integrates Natural Language Processing (NLP) techniques, a Random Forest model, and Geographic Information System (GIS) functionalities to quantify the complex relationship between homestay satisfaction and diverse customer preferences. Notably, this study addresses the positive bias inherent in listing scores by segmenting homestays into three categories (satisfactory, moderate, and dissatisfactory) based on sentiment analysis from online reviews. Furthermore, this study not only identifies eight key determinants of homestay satisfaction but also unveils the nonlinear relationships and interactions between them. More significantly, we identify specific threshold values for geographic determinants, offering actionable recommendations for homestay planning and layout. These findings provide valuable insights that can be leveraged to improve homestay experiences and promote the sustainable development of urban homestays.

The drivers and income effect of big data use by e-commerce farmers: evidence from China

The drivers and income effect of big data use by e-commerce farmers: evidence from China

Novel Genetic Optimization Techniques for Accurate Social Media Data Summarization and Classification Using Deep Learning Models

In the era of big data, effectively processing and understanding the vast quantities of brief texts on social media platforms like Twitter (X) is a significant challenge. This paper introduces a novel approach to automatic text summarization aimed at improving accuracy while minimizing redundancy. The proposed method involves a two-step process: first, feature extraction using term frequency–inverse document frequency (TF–IDF), and second, summary extraction through genetic optimized fully connected convolutional neural networks (GO-FC-CNNs). The approach was evaluated on datasets from the Kaggle collection, focusing on topics like FIFA, farmer demonstrations, and COVID-19, demonstrating its versatility across different domains. Preprocessing steps such as tokenization, stemming, stop word s removal, and keyword identification were employed to handle unprocessed data. The integration of genetic optimization into the neural network significantly improved performance compared to traditional methods. Evaluation using the ROUGE criteria showed that the proposed method achieved higher accuracy (98.00%), precision (98.30%), recall (98.72%), and F1-score (98.61%) than existing approaches. These findings suggest that this method can help create a reliable and effective system for large-scale social media data processing, enhancing data dissemination and decision-making.

Data protection legislation in Africa and pathways for enhancing compliance in big data health research

BackgroundThe increasing availability of large volumes of personal data from diverse sources such as electronic health records, research programmes, commercial genetic testing, national health surveys and wearable devices presents significant opportunities for advancing public health, disease surveillance, personalized medicine and scientific research and innovation. However, this potential is hampered by a lack of clarity related to the processing and sharing of personal health data, particularly across varying national regulatory frameworks. This often leaves researcher stakeholders uncertain about how to navigate issues around secondary data use, repurposing data for different research objectives and cross-border data sharing.MethodWe analysed 37 data protection legislation across Africa to identify key principles and requirements for processing and sharing of personal health and genetic data in scientific research. On the basis of this analysis, we propose strategies that data science research initiatives in Africa can implement to ensure compliance with data protection laws while effectively reusing and sharing personal data for health research and scientific innovation.ResultsIn many African countries, health and genetic data are categorized as sensitive and subject to stricter protection. Key principles guiding the processing of personal data include confidentiality, non-discrimination, transparency, storage limitation, legitimacy, purpose specification, integrity, fairness, non-excessiveness, accountability and data minimality. The rights of data subjects include the right to be informed, the right of access, the right to rectification, the right to erasure/deletion of data, the right to restrict processing, the right to data portability and the right to seek compensation. Consent and adequacy assessments were the most common legal grounds for cross-border data transfers. However, considerable variation exists in legal requirements for data transfer across countries, potentially creating barriers to collaborative health research across Africa.ConclusionsWe propose several strategies that data science research initiatives can adopt to align with data protection laws. These include developing a standardized module for safe data flows, using trusted data environments to minimize cross-border transfers, implementing dynamic consent mechanisms to comply with consent specificity and data subject rights and establishing codes of conduct to govern the secondary use of personal data for health research and innovation.

Compositional Analysis and Mechanical Recycling of Polymer Fractions Recovered via the Industrial Sorting of Post-Consumer Plastic Waste: A Case Study toward the Implementation of Artificial Intelligence Databases.

Nowadays, society is oriented toward reducing the production of plastics, which have a significant impact on the environment. In this context, the recycling of existing plastic objects is currently a fundamental step in the mitigation of pollution. Very recently, the outstanding development of artificial intelligence (AI) has concerned and continues to involve a large part of the industrial and informatics sectors. The opportunity to implement big data in the frame of recycling processes is oriented toward the improvement and the optimization of the reproduction of plastic objects, possibly with enhanced properties and durability. Here, a deep cataloguing, characterization and recycling of plastic wastes provided by an industrial sorting plant was performed. The potential improvement of the mechanical properties of the recycled polymers was assessed by the addition of coupling agents. On these bases, a classification system based on the collected results of the recycled materials' properties was developed, with the aim of laying the groundwork for the improvement of AI databases and helpfully supporting industrial recycling processes.

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.

Research on Blended Teaching of College English Writing Based on 'POA' Theory Under the Background of Big Data

This paper discusses the application value of the writing teaching mode combined with the mixed teaching mode in college English writing teaching against the background of big data. Focusing on production-oriented approach (POA) theory, this paper proposes a mixed learning writing model for English teaching and applies the POA mixed learning English writing teaching model to practical teaching by using the cloud classroom platform, collecting students' big data, and analyzing students' seriousness, classroom participation, satisfaction, and English writing performance. Although the overall student engagement is not high, their classroom satisfaction is very high. Research shows that after the teacher adjusted the lesson plan and encouraged students to participate more in class interactions, their scores improved in the second exam but declined in the third exam . The levels of engagement, participation, and satisfaction have significantly decreased across all grades. The POA combined with the blended learning mode has a great application value in practical teaching in the era of big data.

Ethics of Mathematical Modeling in Public Health: The Case of Medical Male Circumcision for HIV Prevention in Africa

Mathematical modelling has played an increasingly prominent role in public health responses, for example by offering estimates of how infectious disease incidence over time may be affected by the adoption of certain policies and interventions. In this paper, we call for greater research and reflection into the ethics of mathematical modeling in public health. First, we present some promising ways of framing the ethics of mathematical modeling that have been offered in the very few publications specifically devoted to this subject. Second, to draw out some issues that have not yet been sufficiently considered, we bring in the case of mathematical modeling in voluntary medical male circumcision (VMMC) initiatives for HIV prevention in Africa. We argue that greater attention should be paid to ethical considerations in mathematical modeling, particularly as its use is becoming more widespread and its potential impacts are becoming greater in the ‘big data’ era, as witnessed during the COVID-19 pandemic.

From Reactive to Proactive Infrastructure Maintenance: Remote Sensing Data and Practical Resilience in the Management of Leaky Pipes

The introduction of remote sensing technologies, AI and big data analytics in the utility sector is warranted by the need to provide critical services with the least disruption to customers, but also to enable preventive maintenance, extend the life cycle of infrastructure components and reduce grid loss—or overall, to exhibit ‘durability’ and ‘resilience’ when faced with the certainty of breakage and decay. In this paper, we first explore the concept of ‘resilience’ and the nature of practice from a performativist perspective in order to set the scene for discussing the impact of ‘datafication’ on maintenance practices and infrastructure durability. We then describe an instance of introducing remote sensing technologies in district heating network surveillance and leak detection: drone-operated thermographic cameras and underground wire sensors. Based on insights from this case study, we discuss the specificity of data-driven infrastructure maintenance practices, and what it means to exhibit practical resilience in relation to how such practices unfold, interrelate and evolve over time. We reflect on how the use of remote sensing technologies and data analytics (1) potentially changes district heating workers’ epistemic worlds (i.e., how knowledge emerges, is negotiated and ordered in practice) and (2) provides opportunities for ‘messy’ pipe repair work to tacitly adopt proactive and preventive logics to meet continuously evolving organizational and societal needs.

Dietary and Physical Activity Habits of Children and Adolescents before and after the Implementation of a Personalized, Intervention Program for the Management of Obesity.

Obesity in childhood and adolescence represents a major public health problem, mostly attributed to dietary and physical activity factors. We aimed to determine the dietary and physical activity habits of participants before and after the implementation of a personalized, multidisciplinary, lifestyle intervention program for the management of obesity in the context of the Horizon Research Project 'BigO: Big Data against Childhood Obesity'. Three hundred and eighty-six (n = 386) children and adolescents (mean age ± SD: 12.495 ± 1.988 years, 199 males and 187 females) participated in the study prospectively. Based on body mass index (BMI), subjects were classified as having obesity (n = 293, 75.9%) and overweight (n = 93, 24.1%) according to the International Obesity Task Force (IOTF) cut-off points. We implemented a personalized, multidisciplinary, lifestyle intervention program providing guidance on diet, sleep, and exercise, and utilized the BigO technology platform to objectively record data collected via a Smartphone and Smartwatch for each patient. Following the intervention, a statistically significant decrease was noted in the consumption of cheese, cereal with added sugar, savory snacks, pasta, and fried potatoes across both BMI categories. Also, there was an increase in daily water intake between meals among all participants (p = 0.001) and a reduction in the consumption of evening snack or dinner while watching television (p < 0.05). Boys showed a decrease in the consumption of savory snacks, fried potato products, and pasta (p < 0.05), an increase in the consumption of sugar-free breakfast cereal (p < 0.05), and drank more water between meals daily (p < 0.001). Our findings suggest that a personalized, multidisciplinary, lifestyle intervention improves the dietary habits of children and adolescents.

The foresight methodology for transitional shale gas reservoirs prediction based on a knowledge graph

The sedimentary environment in the eastern Ordos area of China is complex and contains a large number of transitional facies environments. Yet, there are many characteristics such as large vertical lithology changes, complex lateral sedimentary environment changes, small monolith thickness and large organic matter content changes, which lead to large uncertainty in the prediction of favorable areas for transitional shale gas. As the intricate reservoirs continue to unfold, the conventional linear prediction methods find themselves facing an arduous path to meet the demands of development. The ever-evolving complexity of these reservoirs has outpaced the capabilities of these traditional approaches. It becomes apparent that a more comprehensive and adaptable approach is necessary to navigate the intricacies of these reservoirs and unlock their hidden potential. Therefore, we put forward a method of introducing knowledge graph into transitional shale gas reservoir prediction in the eastern Ordos region by using big data technology. Because artificial intelligence big data relies heavily on data tags, it is particularly important for the construction of tags. Firstly, a top-down knowledge graph in the field of reservoir prediction is constructed to determine the key parameters used in prediction, namely porosity, total organic carbon and brittleness index. Secondly, the decision tree knowledge graph optimization label is constructed in a bottom-up way. The key parameter of this prediction is the knowledge graph obtained according to the professional knowledge of reservoir prediction, so as to optimize the school label of U-net and reduce the workload of artificial judgment. The results of the combination of the two methods are applied to 11 wells in Daji area of Ordos, and the experimental results are consistent with the actual situation of the reservoir. Based on the foundation of theoretical knowledge, this method enhances the efficiency and accuracy of interpretation and evaluation. It provides fundamental and technical support for the selection of favorable areas in shale gas exploration and the evaluation of exploration and development prospects, particularly in transitional shale gas areas, which is innovative and advanced in the field.