Search Results (532)

With the emergence of data-intensive computing, which is due to the growth of the data produced and generated each day, it became necessary to store and manage big data. Cloud data storage is actually the best choice for large distributed systems. Successful Cloud Computing cannot be achieved without a reliable data-management system to store and handle the enormous volume of data. Management of the available storage system at large scale becomes progressively more complicated, and we face many challenges, such as scalability, data availability, fault tolerance, etc. Also, data storage is faced with specific access patterns: highly concurrent reads of data from the same file, many overwrites, and very concurrent appends to the same file. Most of the existing storage systems use versioning to bring and enhance data access parallelism and this enables better performance levels under concurrency; but, generally, these systems use one component (version manager), which is responsible for generating new versions of each file stored. When we speak in the context of big data, the requests for read, write and append increase. If these requests are managed by a single component, then we have a performance bottleneck and an overloaded version manager. To avoid this drawback, we proposed and designed a new architecture of storage systems that uses versioning; the new architecture uses multi-version managers to support better the scalability and provide partial fault tolerance. To illustrate the practicability of our approach, we assessed it on the BlobSeer data-management system. The experimental results demonstrate that our architecture achieves near-linear scalability for CREATE operations (495 ops/s per additional version manager), reduces WRITE execution time by up to 66%, and maintains 67% availability under single-node failures, all while introducing minimal resource overhead (3% aggregate CPU increase). These results confirm that the proposed multi-version manager architecture offers a practical, scalable, and partially fault-tolerant solution for Cloud data-storage systems. Full article

The security of new energy vehicle (NEV) industry chains has become a strategic issue for industrial competitiveness, the energy transition, and economic security. This study examines how digital economy capabilities jointly support NEV industry chain security across 30 provincial-level administrative regions in China. Drawing on Organizational Information Processing Theory and Dynamic Capability Theory, we conceptualize artificial intelligence capability (AIC), big data analytics capability (BDA), cloud computing infrastructure (CCI), and blockchain application level (BCL) as complementary information-processing and reconfiguration capabilities. We combine Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and Random Forest/SHAP analysis. The revised results show that AIC is a practically necessary condition for supply chain resilience, BDA is a necessary condition for achieving a high cybersecurity level, and BCL is a dimension-specific necessary condition for data security. Four sufficient configurational paths—technology-driven, data-driven, infrastructure-driven, and security-synergistic—lead to high comprehensive NEV industry chain security. Robustness checks using alternative calibration anchors and consistency thresholds show that the core configurations are stable. A revised machine learning specification using only digital economy predictors confirms the high relative importance of AIC. It also shows that the marginal contribution of AIC tends to flatten beyond the upper-middle range. The findings provide a configurational and regionally differentiated perspective on digital economy empowerment while avoiding overgeneralization beyond the Chinese provincial context. Full article

Gridded precipitation datasets are increasingly used as operational tools, with growing emphasis on cloud-native processing to handle multi-decadal archives through reproducible and auditable workflows. This paper presents an end-to-end pipeline that uses Google Earth Engine for the automated extraction of ERA5-Land precipitation, enabling on-the-fly analysis and targeted spatiotemporal data retrieval. The extracted outputs are subsequently evaluated through station-based comparisons using one linear and one non-linear bias-correction technique. The workflow emphasizes scalable data access, consistent station alignment, and distribution-aware diagnostics for extremes. It is designed to support rapid national screening and to provide a transferable blueprint for hydrometeorological applications. Full article

As global markets navigate the dual transition of digitalization and sustainability, the risk of “digital greenwashing” has emerged as a critical corporate governance challenge. Utilizing a comprehensive dataset of Chinese A-share listed firms from 2018 to 2024—an ideal laboratory characterized by rapid regulatory shifts and unique state-market dynamics that provide highly generalizable insights for other emerging economies—this study empirically investigates whether corporate digital transformation acts as a genuine driver for Environmental, Social, and Governance (ESG) enhancement or merely serves as a symbolic disclosure tool. Fortified by rigorous identification strategies, including Propensity Score Matching and Lewbel heteroskedasticity-based instrumental variable estimations, the results confirm that digitalization serves as an incremental yet statistically significant driver for corporate sustainability. Crucially, mechanism analyses reveal a “full moderation” effect: the positive impact of digitalization on ESG performance is completely activated only in the presence of premium external assurance (e.g., Big 4 audits). Without high-quality IT auditing to act as a credibility enforcer and verify the substance of digital signals, technological adoption alone fails to yield significant ESG improvements. Furthermore, a nuanced structural asymmetry is identified: foundational data infrastructures (Cloud Computing and Big Data) directly enhance quantifiable Environmental and Governance metrics, whereas premium audits are strictly required to activate the “soft,” qualitative Social dimension. Finally, the synergy exhibits distinct boundary conditions. It is heavily concentrated within high-pollution industries where digital transition acts as a regulatory survival imperative rather than mere market expansion, and its reliance on external assurance is fundamentally driven by the market-signaling needs of non-State-Owned Enterprises (non-SOEs) rather than the policy-distorted mandates of SOEs. These findings offer critical theoretical extensions and policy implications for standardizing digital-audit infrastructures globally. Full article

The Marmara region of Türkiye, situated along the North Anatolian Fault Zone (NAFZ), constitutes one of the most seismically active and densely monitored zones globally. Given the region’s high vulnerability and the catastrophic impacts of historical events—notably the 1999 İzmit and 2023 Kahramanmara¸s sequences—there is a critical need for advanced seismic hazard risk assessment (SHRA) methods that move beyond static models. This review examines the paradigm shift from traditional geophysics to big data seismology, characterized by the “Five Vs”: volume, velocity, variety, veracity, and value. Critically, we distinguish between two fundamentally different problems: Earthquake Early Warning (EEW), which operates on sub-second timescales after rupture initiation, and probabilistic earthquake forecasting, which operates on timescales of years to decades. The study discusses how cloud-native platforms such as Azure Databricks, combined with data pipelines using Apache Kafka (version 3.5.1) and Apache Spark (version 4.1.2), enable the real-time processing of petabyte-scale seismic sensor streams. Key technological tools, including Physics-Informed Neural Networks (PINNs) and deep learning models such as PhaseNet, are analyzed for their demonstrated ability to enhance EEW systems through sub-second phase picking and automated event detection. Seismic tomography is also undergoing AI-enabled transformation, yielding higher-resolution subsurface imaging. We present statistical validation metrics and uncertainty quantification methods essential for credible hazard assessment. By addressing computational bottlenecks through hybrid computing architectures and edge computing, this framework aims to improve the warning lead time for Istanbul’s critical infrastructure. This work provides a structured roadmap for bridging the gap between traditional seismic data analysis and operational predictive analytics in the Marmara region. Full article

The world evolves continuously across space and time. Massive volumes of data are generated through sensing, simulation, remote observation, and human activities, capturing dynamic processes in environmental, social, economic, and engineered systems. Critical insights are embedded within these large-scale spatiotemporal datasets. Spatiotemporal Data Science provides a conceptual and methodological framework for analyzing such data by integrating spatiotemporal thinking, computational infrastructure, artificial intelligence, and domain knowledge. The field advances methods for data acquisition, harmonization, modeling, visualization, and decision support, enabling applications in natural disaster response, public health, climate adaptation, infrastructure resilience, and geopolitical analysis. By leveraging emerging technologies—including generative Artificial Intelligence (AI), large-scale cloud platforms, Graphics Processing Unit (GPU) acceleration, and digital twin systems—Spatiotemporal Data Science enables scalable, interoperable, and solution-oriented research and innovation. It represents a critical frontier for scientific discovery, engineering advancement, technological innovation, education, and societal benefit. Spatiotemporal Data Science is a transdisciplinary field that studies and models dynamic phenomena across space and time by integrating spatial theory, temporal reasoning, artificial intelligence, and scalable computational infrastructure. It enables the development of adaptive, predictive, and increasingly autonomous systems for understanding and managing complex real-world processes. Full article

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article

Spontaneous combustion in goaf areas poses a significant threat to coal mine safety. Traditional safety management systems, reliant on passive response and single-indicator thresholds, often suffer from delayed warnings and lack cognitive decision support. To address this challenge, this study proposes a big-data-driven cognitive computing framework for dynamic risk prediction of goaf spontaneous combustion, based on a “Cloud-Edge-End” collaborative architecture. The method leverages multi-sensor big data streams (CO, C₂H₄, O₂, etc.) and deploys a lightweight Radial Basis Function (RBF) neural network on underground edge computing nodes (STM32) for real-time analytics. The model demonstrates excellent predictive performance on imbalanced datasets, with a PR-AUC of 0.910 and a recall of 99.7%. The edge-deployed RBF model achieves a single-pass inference time of only 0.62 ms, enabling real-time cognitive risk mapping. Field application at Z Coal Mine validated the system’s effectiveness, providing an average pre-warning time of 48.5 h, achieving zero spontaneous combustion accidents, and reducing the Total Recordable Injury Rate (TRIR) by 15.2%. This work illustrates how edge-based cognitive computing can transform safety management from passive response to proactive prevention, offering a scalable and interpretable framework for intelligent mine safety. Full article

We researched, analyzed and predicted building energy consumption data using cloud computing and constructed an intelligent model. A local outlier factor outlier discovery algorithm was created to monitor abnormal energy consumption. A random forest algorithm was used for high-dimensional data to predict building energy consumption and analyze data in the Commercial Building Energy Consumption Survey database. The degree of importance of independent variables was evaluated to analyze how the architectural attributes of office buildings affect energy consumption. Full article

In the era of big data, data is key to the accuracy of analytical models, and cloud computing services are often used to efficiently process large volumes of data. However, outsourcing sensitive data to a third-party cloud service provider results in a loss of direct control over the data, raising serious security concerns. The target of this study is to propose highly efficient and privacy-preserving protocols that compute the maximum/minimum value in large-scale data. To achieve the improvements in efficiency, the proposed protocols reuse the intermediate results generated in independent subprotocols. Existing privacy-preserving maximum/minimum protocols are based on approximation methods that sacrifice accuracy or reveal information during execution. They use costly comparison operations that are proportional to the size of the input data and are not suitable for large-scale data applications. In contrast, the proposed protocols theoretically reduce the number of communication rounds by 25%, the communication size by 50%, and the computational cost by 42% compared to the existing protocols. Nevertheless, the accuracy and privacy are fully maintained. In order to demonstrate these efficiency improvements concretely, we conducted experiments and demonstrated that the proposed protocols reduce the communication volume by half and the execution time by 22%. Because the proposed protocols support parallel execution, their performance can be substantially enhanced in cloud environments that provide large-scale parallel processing resources. Even data owners with restricted computational capabilities can use the protocols without exposing their information. Under the secure version, even cloud servers executing the protocol learn nothing about the input data or the computation results. Full article

This study presents a systematic review of the role of key technologies in advancing sustainable logistics and supply chain management. Specifically, it explores the integration of Industry 4.0 (I4.0), logistics 4.0, and digital supply chains, focusing on technologies such as artificial intelligence (AI), augmented reality (AR), big data analytics (BDA), blockchain, cloud computing (CC), industrial internet of things (IIoT), machine learning (ML), robotics, virtual reality (VR), and internet of things (IoT). The aim is to examine how these technologies contribute to green logistics (GL), green supply chain management, sustainability, and the circular economy (CE). Data were collected from the Scopus database, covering studies published between 2019 and 2024. A total of 1471 publications were initially identified, and 39 studies met the selection criteria. The PRISMA approach was employed for the systematic review, revealing that leading research on I4.0 is concentrated in top-tier journals, with a significant number of publications from Italy focusing on digitalization in the agriculture and food sectors. Systematic literature reviews and resource-based theory are predominant, yet there is a notable gap in aligning research with the United Nations Agenda 2030 Sustainable Development Goals (SDGs). This paper provides insights into technological adoption trends and offers recommendations for industry leaders seeking to enhance sustainability, eco-friendliness, and alignment with the SDGs within their supply chains. Full article

The dynamic development of Industry 4.0 technologies, referred to as smart manufacturing technologies (SMTs), is significantly changing both production systems and quality management practices. The aim of this article is to analyse the impact of smart manufacturing technologies on the seven principles of quality management (QMP). The research is based on a narrative, semi-systematic review of the literature from the Web of Science and Scopus databases from the last seven years, using thematic analysis. Traditional interpretations of QMP principles were compared with new conditions resulting from the implementation of technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, vision systems, virtual and augmented reality, and additive manufacturing. The results indicate that SMTs do not eliminate quality management principles, but significantly change the way they are implemented. There is a shift towards product personalisation, shorter product life cycles, decentralised decision-making, flexible and autonomous processes, digital surveillance, and intensive use of real-time data. The article argues that SMT and QMP are complementary approaches—technologies increase the effectiveness and efficiency of quality management, but do not replace it. The considerations presented here are a starting point for further empirical research on the new ‘Quality 4.0’ model in the intelligent manufacturing environment. Full article

Disputes become an accepted reality of construction projects, often resulting in serious consequences, including time and cost overruns, and broader macroeconomic impacts on the national economy. Disputes need to be managed effectively to reduce their negative impacts. Recently, an increasing trend has emerged toward integrating dispute management practices with innovative technologies of the digital era. Therefore, this research aims to investigate the applications of emerging digital technologies and Artificial Intelligence (AI) to manage disputes proactively. This research begins with a scientometric analysis, followed by a systematic review of dispute management using digital technologies with a special focus on AI. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, the systematic review identified 66 previous studies that combine dispute management and digital technologies. The analysis revealed that technologies such as Artificial Intelligence (AI), Building Information Modelling (BIM), blockchain, smart contracts, Document Management System (DMS), big data, cloud computing, and Unmanned Aerial Vehicle (UAV) are utilized, while AI and its technologies significantly contribute to managing disputes. AI technologies, especially natural language processing, show a growing trend in applications for predicting disputes using project documents. In addition, the study develops a conceptual framework to predict disputes using AI technologies. The study identified potential research areas involving the integration of digital technologies for dispute management in the construction industry, offering valuable direction for future research. The research suggests that the use of AI and emerging digital technologies potentially predicts and mitigates disputes, thereby paving the way for proactive dispute management. Full article

This study investigates the performance consequences of adopting emerging digital technologies such as artificial intelligence, machine learning, big data analytics, and cloud computing, with attention to financial, operational, and environmental dimensions. Using an event study of 134 adoption announcements by publicly traded U.S. firms from 2009 to 2019, we compare adopters with matched control firms identified through propensity score matching. The empirical evidence shows that adoption is followed by gains in profitability and market valuation, reflected in improvements in return on assets, return on equity, and Tobin’s Q, alongside higher inventory turnover. At the same time, adopting firms exhibit a measurable decline in greenhouse gas emissions when compared with matched control firms. Taken together, these results suggest that digital transformation can align economic performance with environmental improvement, rather than forcing firms to choose between the two. The findings therefore provide practical guidance for managers and policymakers seeking to evaluate digital investments through the lens of long-term sustainability. Full article