Big Data Research Articles

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study

Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings.

The role of big data in transforming financial management in U.S. healthcare: A conceptual framework

This paper explores the transformative role of big data in financial management within the U.S. healthcare sector, highlighting its capacity to address long-standing financial inefficiencies, enhance decision-making, and improve regulatory compliance. Big data technologies, such as predictive analytics and real-time financial reporting, provide healthcare institutions with the tools to forecast financial trends, optimize budgeting, and mitigate risks. Despite the significant benefits, the adoption of big data in healthcare finance faces challenges, including data privacy concerns, regulatory complexities, and technological limitations. The paper examines these barriers and enablers, offering strategic recommendations for healthcare institutions and policymakers to optimize the use of big data. By improving data governance, upgrading infrastructure, and fostering collaboration across departments, healthcare organizations can maximize the impact of big data on their financial operations. The review concludes with a call for continued policy support and financial incentives to encourage the broader adoption of big data in healthcare finance, ultimately promoting cost efficiency and operational resilience in the sector. Keywords: Big Data, Healthcare Financial Management, Predictive Analytics, Data Governance, Regulatory Compliance, Financial Decision-Making.

Developing innovative financial products for sustainable economic growth

In today’s competitive landscape, technology firms increasingly turn to advanced data analytics as a strategic tool for boosting revenue growth and operational efficiency. This review explores how leveraging sophisticated analytical techniques can enable these organizations to extract actionable insights from vast datasets, thereby enhancing decision-making processes and driving business performance. Advanced data analytics encompasses a range of methodologies, including predictive analytics, machine learning, and big data analysis, which collectively empower firms to identify trends, optimize resources, and tailor products to meet evolving customer demands. One of the primary benefits of utilizing advanced data analytics is the ability to enhance revenue growth through improved customer targeting and segmentation. By analyzing customer behaviors and preferences, technology firms can develop personalized marketing strategies that resonate with their target audiences, resulting in increased conversion rates and customer loyalty. Moreover, data analytics facilitates the identification of new market opportunities, enabling firms to innovate and expand their product offerings effectively. Operational efficiency is another critical area where advanced data analytics proves invaluable. By monitoring key performance indicators (KPIs) and operational metrics, technology firms can streamline their processes, reduce costs, and minimize waste. Predictive analytics, in particular, allows organizations to anticipate potential disruptions in their operations, enabling proactive measures to mitigate risks and maintain smooth workflows. This capability not only enhances productivity but also fosters a culture of continuous improvement. Additionally, advanced data analytics supports informed strategic planning by providing insights into market dynamics and competitive positioning. Firms can leverage these insights to make data-driven decisions, allocate resources effectively, and align their business strategies with market trends. In conclusion, advanced data analytics serves as a catalyst for revenue growth and operational efficiency in technology firms. By embracing these analytical techniques, organizations can harness the power of data to drive innovation, improve customer engagement, and achieve sustainable competitive advantage. As technology continues to evolve, the strategic implementation of advanced data analytics will be essential for firms seeking to thrive in an increasingly data-driven world. Keywords: Advanced Data Analytics, Revenue Growth, Operational Efficiency, Technology Firms, Predictive Analytics, Customer Targeting, Strategic Planning.

Evaluating third places, walkability, and positive mood in Los Angeles, USA: using social media data

ABSTRACT Past studies have stressed the importance of designing urban neighborhoods to be walkable and accessible to local amenities; however, the psychological benefits of urban design have been vastly understudied. The current study integrates theoretical insights from urban planning and environmental psychology literature in analyzing spatial relationships between third places, walkability, and positive mood across Los Angeles city blocks. To measure positive mood, this study utilized geotagged social media (Twitter) data to conduct sentiment analysis on tweets, using mood as a proxy for psychological well-being. Regression analyses revealed the independent and joint positive influences of third places and walkability on the expected number of positive tweets. The results indicated that more positive tweets were expected on blocks that (a) were more walkable and (b) had more third places. Notably, the number of third places on focal and surrounding blocks mattered more for psychological well-being on less walkable blocks. This research shows how big data can be leveraged by urban planners to make sense of human – environment relationships and the neighborhood context of well-being.

Digital Transformation and Innovation of the News Media

Under the background of globalization and the rapid development of information technology, the digital transformation and innovation of news media have become an important trend of the industry. Traditional media adapt to the new audience needs and market environment through digital transformation, and gradually transform to diversified content production, personalized recommendation, multi-platform distribution and paid subscription mode. Many media organizations, such as the New York Times and the BBC, have explored the possibilities of content innovation and dissemination through artificial intelligence, big data and virtual reality. However, digital transformation also faces challenges such as information authenticity, privacy security and technology dependence. In the future, with the further development of new technologies, news media will continue to explore AI-driven news production, immersive reporting and blockchain applications to build a more innovative, efficient and credible news ecosystem.

Exploring advanced techniques in process automation and control: A generic framework for oil and gas industry applications

This paper explores advanced techniques in process automation and control, offering a generic framework tailored for applications within the oil and gas industry. The framework addresses the growing need for operational efficiency, safety, and sustainability in an industry characterized by complex, high-risk processes. The adoption of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Industrial Internet of Things (IIoT) has revolutionized process control systems, enabling real-time monitoring, predictive maintenance, and optimization of production assets. Advanced Process Control (APC) techniques, including model predictive control (MPC), multivariable control systems, and data-driven analytics, play a pivotal role in enhancing production, minimizing downtime, and reducing operational costs. This generic framework leverages the integration of AI and big data analytics for optimizing control strategies, ensuring accurate decision-making and improving operational responsiveness to changing conditions. Additionally, the paper examines how automated control systems mitigate risks associated with human error, while enhancing safety protocols by providing early warnings and real-time insights into equipment performance. Automation also supports the industry's shift towards more sustainable practices by improving energy efficiency, minimizing environmental impact, and enabling remote monitoring in hazardous locations. Through case studies and examples, the paper illustrates the successful implementation of these techniques in upstream, midstream, and downstream operations, highlighting the significant role of digital transformation in driving innovation. The proposed framework can be adapted to diverse oil and gas settings, serving as a foundation for developing scalable, flexible, and sustainable automation solutions. By outlining the key components and benefits of advanced process automation and control, this study contributes to the ongoing discourse on how technological advancements can reshape operational strategies in the oil and gas sector. Keywords: Process Automation, Control Systems, Oil And Gas Industry, Artificial Intelligence (AI), Machine Learning (ML), Industrial Internet of Things (IIoT), Advanced Process Control (APC), Model Predictive Control (MPC), Predictive Maintenance, Digital Transformation, Sustainability.

The Intelligent Data Ecosystem: Uniting AI and Data Integration to Revolutionize Data Science

The Rise of Big Data: Today, the scale and domain of massive data management has prolonged to an unmatched state. In this paper, we will examine the process of data science developed over the recent past and argue that artificial intelligence (AI) and data integration converge to create intelligent data ecosystem reaching new heights. This paper combines AI's strength to automate analytics and data integration, which is able to harmonize different data sources and shows how businesses can benefit from a unified approach for innovation, operational efficiency, decision-making. We also cover some of the key use cases in the industry, and ways these technologies are gaining momentum and reshaping the future of data science.

BIG DATA AND STATISTICAL APPLICATIONS

In recent years, modern developments in computer and cloud technologies have led to a significant increase in the quantity and speed of generated and stored information. This increase in the amount of information has introduced a new concept into our lives called "Big Data". Big Data offers significant advantages, especially for governments and businesses, in making informed decisions about their futures. However, the magnitude and diversity of this emerging data also bring about some challenges. Traditional database systems have been inadequate in processing these data formats, necessitating the need for new tools and techniques to overcome this challenge. Today, a wide range of techniques and technologies have been developed to collect, process, analyze, and visualize “Big Data”. These techniques and technologies encompass and draw upon various fields such as statistics, computer science, applied mathematics, and economics. Keywords: big data, visualization of data, structured data, big data technologies, big data techniques.

A FUZZY APPROACH FOR INFORMATION QUALITY ASSESSMENT IN BIG DATA ENVIRONMENT

Information quality assessment is very important for enterprise development, especially under big data environment. This paper combines the characteristics of big data to construct an information quality evaluation index system in a big data environment. The information quality assessment system includes fourteen indicators consisting of four aspects: reliability, relevance, timeliness, completeness, etc. Then, we set up Five examples to test and verify the information quality evaluation index system with a fuzzy approach, and the examples demonstrate the rationality and feasibility of the evaluation index system. The results of the study have certain theoretical and practical significance for the assessment of the quality of accounting data in a big data environment. Keywords: big data, data quality, fuzzy set, fuzzy logic, fuzzy approach.

Unveiling the strategic impact of big data analytics capabilities in the Saudi Arabian banking sector: an explorative approach

Purpose The purpose of this study is to identify a critical pathway of the effect of big data analytics capabilities (BDACs) on strategic vigilance based on hierarchical process and a capability approach. Design/methodology/approach The researcher adopted a qualitative approach using interviews and a quantitative approach based on the interpretative structural modeling (ISM) fuzzy cross-impact matrix multiplication applied to classification (MICMAC) approach. A primary theoretical approach was also conducted to identify BDACs previously cited in the literature. Findings Four main subdivisions of BDACs were identified: management capabilities, infrastructure flexibility, talent capability and technology. Management capabilities followed by big data technical knowledge and associated with talent capabilities generate a flexible infrastructure to enhance SV. A dynamic capability perspective of knowledge and information is also required for SV. Research limitations/implications Despite the opportunity of this research and the originality of results, some limitations have to be mentioned and can constitute further directives for future researchers, such as the problem of result generalization. First, this research was based in Saudi Arabia, and a comparative approach to defining BDAC on an international level can be more beneficial in providing an exhaustive list of these capabilities. Second, reliability issues, in this research can be addressed due to the use of qualitative data collection which is considered by many researchers as unspecified and can lack scientific rigor. Future studies can improve the number of interviews during the data collection process and data process using an advanced methodological approach. Third, the effect of BDAC in SV according to the hierarchical final modal is not quantified, future work can use this research model to appreciate each effect using a quantitative approach such as correlation and structural equation modeling while considering respondents with different profiles to take into account different point of view in this concern. Practical implications This research enriches the BDAC and MICMAC literature and contributes to this aspect in three main levels. First, by providing an additional empirical asset in this field, this study offers by the way a new case to the big data literature on the banking sector. Based on the limited knowledge as well as results collected from different databases and rigorously analyzed, this subject was not treated previously and the author could not find similar studies with the same approach dealing with the key BDACs in Saudi Arabia. Social implications This research presents three main implications for policymakers and researchers interested in big data analytics (BDA) through a capability and strategic perspective. First, to attain SV, they should prioritize the development of interactive interfaces and open platforms as the primary step before collecting information and deconstructing it to guarantee the generation of knowledge and make decisions effectively. Second, policymakers must introduce organizational technologies in terms of technology management, technical knowledge and technology for decision-making. This requires simultaneous sharing and communication according to relational management. Third, the research conclusions have many critical managerial ramifications for banks in Saudi Arabia while considering the adoption of BDAC. The importance of BDACs (especially technical aspects) in shaping the decision-making to be strategically vigilant emphasizes policymakers’ orientation by paying close attention to these aspects and specific training programs to facilitate the use of such technologies and guarantee strong security measures. Moreover, findings support a balance between technical and functional BDAC. Originality/value The adoption of a knowledge-based dynamic capabilities (KBDCs) view to analyze the interaction between different BDACs in banks in Saudi Arabia to be strategically vigilant using a mixed approach.

Empowering big data analytics through machine learning: Applications, challenges, and future directions

Abstract. This paper explores the transformative role of machine learning (ML) methodologies in big data analytics, highlighting supervised learning, unsupervised learning, and deep neural networks' contributions to diverse sectors. Supervised learning, with regression analysis at its core, provides accurate forecasting in finance and healthcare by modeling relationships between variables. Unsupervised learning, through techniques like k-means and hierarchical clustering, uncovers patterns within data, offering insights for retail and biological analysis. Deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), excel in complex tasks like image recognition and sequential data processing, driving advances in fields from autonomous driving to language translation. The application of ML in enhancing business intelligence, innovating fintech, and advancing healthcare analytics is discussed, alongside the challenges of data privacy, security, ethical considerations, and the skills gap. This paper underscores the need for advanced cryptographic techniques, bias mitigation strategies, and education to address these challenges. It concludes by emphasizing the importance of interdisciplinary education and AI advancements in bridging the skills gap, ensuring the ethical use of ML, and making these technologies accessible for future innovations.

Cultural big data: nineteenth to twenty-first century panoramic visualization

From the nineteenth-century panorama to the emergence of the digital panoramic format in the 1990's, the visualization of large images frequently relies on panoramic viewing strategies. Originally rendered in the form of epic painted canvases, these strategies are now amplified through gigapixel imaging, computer vision and machine learning. Whether for scientific analysis, dissemination, or to visualize cultural big data, panoramic strategies pivot on the illusion of immersion. The latter is achieved through human-centered design situated within a large-scale environment combined with a multi-sensory experience spanning sight, sound, touch, and smell. In this article, we present the original research undertaken to realize a digital twin of the 1894 panorama of the battle of Murten. Following a brief history of the panorama, the methods and technological framework systems developed for Murten panorama's visualization are delineated. Novel visualization methodologies are further discussed, including how to create the illusion of immersion for the world's largest image of a single physical object and its cultural big data. We also present the visualization strategies developed for the augmentation of the layered narratives and histories embedded in the final interactive viewing experience of the Murten panorama. This article offers researchers in heritage big data new schemas for the visualization and augmentation of gigapixel images in digital panoramas.

Learning Analytics Based on Big Data: Student Behavior Prediction and Personalized Educational Strategy Formulation

Abstract: Learning analytics have become a game-changer in education by using big data to analyse student behaviours, predict student outcomes and provide personalised interventions. This paper outlines the main components in learning analytics including data collection, predictive modelling and personalised educational strategies. It demonstrates how predictive models can be used to identify at-risk students and why real-time feedback can keep students engaged and motivated. Two case studies and examples of the data are used to illustrate how institutions can shift from reactive to proactive mode using learning analytics to track engagement, performance, and personalise the learning path. The study also shows that learning technologies are becoming adaptive to personalise learning experience, which results in a more learner-centric approach to education catering for the individual needs of students. Overall, the study demonstrates the role of learning analytics in creating a data-driven environment, which improves the student learning success and retention by addressing the challenges ahead of time.

DIGITAL TRANSFORMATION IN SUPPLY CHAINS: BENEFITS, CHALLENGES AND TECHNOLOGICAL INNOVATIONS

Digitalization is revolutionizing supply chains, resulting in significant gains in efficiency and competitiveness. This study investigates the role of digital technologies, such as the Internet of Things (IoT), artificial intelligence (AI), big data, and blockchain, in transforming supply chain management. The research presents a review of the current literature and analyzes case studies of companies that have adopted these innovations. The results show that the integration of these technologies allows for better visibility into processes, facilitating informed and agile decision-making. The automation of routine tasks and demand prediction through AI algorithms contribute to cost reduction and waste minimization. In addition, the use of digital platforms improves collaboration between the various actors in the chain, optimizing the flow of information. However, the study also highlights the challenges associated with digitalization, such as resistance to change, the need for significant investments, and the shortage of technical skills. The analysis concludes that, despite the barriers, the benefits of digitalization outweigh the challenges, making it essential for the sustainability and growth of supply chains.

Big Data Analytics for Forecasting Tourism Recovery in Bali Island Using Multivariate Time Series

Bali is a famous tourist area and can significantly contribute to the Indonesian tourism sector. The COVID-19 pandemic has made Indonesian tourism, including Bali tourism, experience a decline. In March 2022, COVID-19 cases decreased, and the government began to relax some policies. The tourism sector is vital in economic recovery efforts after the COVID-19 pandemic. Therefore, it is necessary to identify tourism recovery to determine strategies and policies related to Indonesian tourism, especially in Bali. Multivariate time series forecasting of tourism demand can be used to identify tourism recovery using several significant data sources. The methods used are Vector Autoregressive (VAR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data used are the monthly official number of tourists, room occupancy rate, Google Trends, number of booking.com user reviews, and nighttime light intensity in Bali Province from January 2019 to December 2022. The results show that the best forecasting method is VAR, and modeling with multivariate time series forecasting can improve the performance of forecasting results. In addition, big data can be used as a source of supporting data that can provide better forecasting results, and the size of the dataset affects the selection of the best model. Furthermore, the descriptive and forecasting analysis results show that Bali tourism has experienced post-pandemic tourism recovery. The strategies and policies of the Bali government to restore Bali tourism faster are good enough.

Pharmacodynamic evaluation and network pharmacology analysis of a novel anti-heat stress Chinese herbal formula

Frequent extreme heat events around the world not only pose a major threat to human health but also cause significant economic losses to the livestock industry. The existing management practices are insufficient to fully prevent heat stress with an urgent need to develop preventive medicines. The aim of this study was to develop an anti-heat stress Chinese herbal formula (CHF) via big data analysis techniques and to evaluate its anti-heat stress effect and mechanism of action via pharmacodynamic evaluation and network pharmacology analysis. Many anti-heat stress CHFs were collected from the Chinese National Knowledge Infrastructure (CNKI) database. Three alternative CHFs were obtained via unsupervised entropy hierarchical clustering analysis, and the most effective CHF against heat stress, Shidi Jieshu decoction (SJD), was obtained by screening in a mouse heat stress model. In dry and hot environments, SJD significantly improved the heat tolerance of AA broilers by 4–6°C. In a humid and hot environment, pretreatment with 2% SJD resulted in 100% survival of Wenchang chickens at high temperatures. The main active ingredients of SJD were identified as muntjacoside E, timosaponin C, macrostemonoside H and mangiferin via ultra-performance liquid chromatography/mass spectrometry (UPLC/MS) and database comparison. The active ingredients of SJD were found to target tumor necrosis factor-α (TNF-α), signal transducer activator of transcription 3 (STAT3) and epidermal growth factor receptor (EGFR). Finally, the safety of the new formulation was assessed in an acute oral toxicity study in rats. The SJDs developed in this study provide a new option for the prevention of heat stress in animal husbandry and offer new insights for further research on anti-heat stress.

Machine Learning-Based Financial Big Data Analysis and Forecasting: From Preprocessing to Deep Learning Models

Abstract: Machine learning technology has revolutionized the financial sector by allowing faster and more accurate analysis and forecasting of large-scale financial data. This paper focuses on how machine learning ( ML ), especially deep learning models, can help to deal with high dimensional, noisy, and non-stationary financial data. Essential methodologies such as data preprocessing, feature engineering, and dimensionality reduction are imperative for preparing the raw financial data to ML algorithms. Methods such as outlier detection, normalisation for preprocessing, and feature (variable) selection for dimensionality reduction improve the models accuracy and efficiency. The paper also examines how deep learning models, such as Recurrent Neural Networks ( RNN ) and Long Short-Term Memory ( LSTM ) networks, can overcome the issues of autoregressive integrated moving average ( ARIMA ) models for financial time series prediction. An in-depth comparison of the machine learning models, ranging from supervised to unsupervised methods, is also provided to discuss their pros and cons in the financial domain, including popular applications such as credit scoring, fraud detection, and market risk prediction. The study finally concludes by discussing how optimisation methods such as hyperparameter tuning and cross-validation are imperative for ML models in complex financial scenarios to ensure their generalisation capability and avoid overfitting.

Movie Recommendation System Based on Word Embedding

Abstract. Amidst the swift progression of Internet and big data technologies, recommendation systems have emerged as crucial conduits linking users to products across various digital platforms. This study delves into the deployment of neural collaborative filtering within the realm of movie recommendation systems, with the objective of constructing a system of high precision. Utilizing the MovieLens dataset, this investigation applies one-hot encoding and embedding techniques within the PyTorch-Lightning framework to effectively model user behaviors and predict cinematic preferences. The neural collaborative filtering (NCF) model leverages deep learning to extract latent features of users and items, exhibiting a notable enhancement in performance compared to traditional collaborative filtering approaches. Empirical results indicate that the NCF model attains an accuracy of 86% on the validation dataset, underscoring its efficacy in extensive recommendation scenarios.

Data mining in AI: Evolution, applications, and future directions

Abstract. This paper provides a comprehensive analysis of the evolution and impact of data mining in the field of artificial intelligence (AI), with a particular focus on its application within social and information networks. It traces the origins of AI back to the 1956 Dartmouth Conference, highlighting the subsequent advancements in technologies such as machine learning and data mining that have fueled AI's growth. The paper explores the multifaceted applications of data mining in various sectors including healthcare, transportation, and industrial manufacturing, and delves into the challenges and innovations in recommendation systems, matrix factorization, and intelligent control of autonomous vehicles in intelligent transportation systems. The study emphasizes the significance of distributed algorithms and big data processing frameworks in enhancing the efficiency and applicability of data mining techniques.