Abstract

Large-scale monitoring systems generate vast streams of events from heterogeneous sources such as distributed servers, network devices, sensors, and application logs. Extracting meaningful insights from these events in real time is critically important for fault detection, performance optimization, security monitoring, and adaptive system management. Distributed event correlation and analysis techniques are designed to process high-volume, high-velocity event streams, identifying patterns, dependencies, and anomalies that signify relevant system states. This paper explores the design, implementation, and evaluation of distributed correlation models and analytical frameworks that support real-time and near-real-time monitoring in large-scale environments. We examine architectural patterns such as event streaming, complex event processing (CEP), distributed rule engines, and machine learning-based correlation models. The study also addresses challenges including scalability, event heterogeneity, latency, and accuracy. Through an extensive literature review and methodological synthesis, we identify key trade-offs between centralized and distributed correlation mechanisms, discuss data partitioning strategies, and highlight how analytical techniques such as probabilistic modeling, clustering, and graph-based correlation support effective interpretability. Results from comparative analyses show that distributed event correlation significantly improves responsiveness and fault detection accuracy in large-scale systems compared to traditional centralized approaches. Finally, the paper proposes future directions for hybrid analytical frameworks and scalable event processing infrastructures.