The rapid growth of enterprise systems and cloud computing has transformed data management across hybrid environments integrating on-premise databases, private clouds, and public cloud infrastructures. However, challenges such as data consistency, latency, conflict resolution, security, and fault tolerance remain critical in distributed heterogeneous systems. Traditional synchronization methods are often inadequate for dynamic real-time workloads. This study reviews intelligent data synchronization techniques for hybrid data platforms, emphasizing AI- and machine learning-based approaches that enhance synchronization efficiency, scalability, and reliability. The proposed framework includes four layers: Data Acquisition, Intelligent Synchronization Engine, Adaptive Conflict Management, and Distributed Analytics. Predictive learning algorithms optimize synchronization timing and resource allocation, while adaptive conflict resolution mechanisms minimize inconsistencies. Experimental results show that intelligent synchronization methods reduce delay, improve throughput, enhance scalability, and strengthen failure recovery compared to traditional approaches. The study concludes that AI-driven synchronization is essential for real-time analytics, distributed transactions, and scalable cloud-native applications in modern enterprise environments.
J. Arsac, Gérard Huet· International Journal of Dat...· 0 citations
The manufacturing sector is undergoing a paradigm shift with the integration of Industry 4.0 technologies, particularly Industrial Big Data Analytics (BDA), which leverages high-velocity data from IoT sensors, PLCs, and production systems to enable real-time decision-making. This paper presents a novel edge-cloud analytics framework designed to address critical challenges in modern manufacturing, including data heterogeneity, latency bottlenecks, and cybersecurity risks. By implementing a hybrid architecture, the system processes sensor data at the edge (e.g., vibration spectra, thermal images) with <50ms latency for time-sensitive tasks like defect detection, while cloud-based machine learning models (e.g., LSTMs) perform long-term predictive maintenance with 89% accuracy. A large-scale case study conducted at an automotive assembly line demonstrated a 20% increase in production throughput and 15% reduction in unplanned downtime, translating to $2.7M annual cost savings. Key innovations include: (1) a dynamic data normalization pipeline (Eq. 1) that handles skewed industrial datasets; (2) a comparative analysis of ML models, showing Random Forest outperforms ANN/SVM in defect classification (92.4% F1-score); and (3) a priority-based edge processing system that reduces cloud bandwidth usage by 60%. Despite these advancements, the study identifies persistent hurdles such as legacy system interoperability (resolved via OPC UA gateways) and adversarial robustness in edge ML models. The paper concludes with a roadmap for future work, including federated learning for multi-plant scalability and digital twin integration for simulation-driven analytics. These findings validate BDA as a transformative tool for smart manufacturing, offering a 5.2-month ROI and actionable insights for practitioners adopting Industry 4.0 solutions.
J. Arsac, Gérard Huet· International Journal of Dat...· 0 citations
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