A Scalable Real-Time Event Prediction System for Distributed Networks Using Online Random Forest and CluStream
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Abstract
This paper presents a robust architecture designed for real-time event prediction in distributed networks, utilizing Online Random Forest (ORF) and CluStream for incremental learning and dynamic clustering. The system addresses challenges posed by high-velocity, large-scale data streams, incorporating adaptive sliding windows and real-time data processing to ensure scalability, low latency, and accuracy. Comparative analysis against traditional models, including Naive Bayes and Support Vector Machines, reveals that the proposed system achieves superior predictive accuracy (91.5%), precision (92%), and recall (88%) while maintaining an F1 score of 90%. Clustering efficiency is significantly improved through CluStream, which dynamically manages evolving data streams with lower clustering time compared to conventional methods like K-Means. However, as data stream size increases, latency grows from 120ms for small streams (10MB) to 850ms for large streams (1000MB), indicating a need for further optimization at extreme scales. The system is suitable for applications in network security, IoT monitoring, and large-scale real-time analytics. Despite its strengths, limitations include resource consumption and challenges in managing highly volatile or unstructured data. Future enhancements may focus on reducing latency for larger data streams and improving adaptability to extreme concept drift. This research demonstrates a scalable, efficient, and adaptive approach to real-time event prediction in distributed environments.
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