Big Data Meets Graph Theory: ML-Based Insights for Scalable Distributed Systems

Abstract

The exponential growth of big data and its increasing complexity demand scalable, efficient, and intelligent approaches for processing and analysis in distributed systems. This paper explores the convergence of big data analytics, graph theory, and machine learning (ML) to address the inherent challenges of scalability, resource optimization, and real-time insights in distributed computing environments. A novel ML-based framework is proposed, leveraging graph theory principles to model data relationships, dependencies, and workflows across distributed nodes. The framework integrates graph embeddings, clustering techniques, and predictive analytics to enhance data locality, reduce computational latency, and improve load balancing. Experimental evaluations conducted on large-scale distributed systems reveal a significant improvement in performance metrics, including a 35% reduction in task execution time and a 40% enhancement in resource utilization. This study establishes a foundation for scalable and adaptive systems capable of addressing the evolving demands of big data ecosystems, offering actionable insights for researchers and practitioners