Distributed Computing Meets Graph Theory: Enhancing Machine Learning Workflows for Real-Time Big Data Applications

Abstract

The convergence of distributed computing and graph theory presents a powerful paradigm for optimizing machine learning workflows in real-time big data applications. As the demand for processing massive datasets in real-time grows, traditional approaches face significant challenges related to scalability, resource allocation, and processing speed. This paper explores the integration of distributed computing architectures with graph-based algorithms to enhance the efficiency of machine learning models, specifically focusing on big data applications. Graph theory provides a natural representation for relationships within complex datasets, and when combined with distributed computing, it allows for parallelized processing, ensuring scalability and reduced latency. This study investigates the application of graph-based models in distributed machine learning workflows, highlighting their effectiveness in managing large, dynamic datasets across multiple nodes. We demonstrate how leveraging the graph structure can accelerate data processing, enhance model training, and improve real-time decision-making. Through experimental evaluation on benchmark datasets, the paper showcases the performance improvements in both the throughput and efficiency of machine learning tasks. The proposed framework not only enhances the computational capacity of machine learning systems but also offers a pathway for addressing the challenges of big data analytics in distributed environments. Our findings indicate that the integration of graph theory with distributed computing can provide significant advancements in the optimization and scalability of machine learning workflows in real-time big data applications.