Leveraging AI for Scalable Query Processing in Big Data Environments

Abstract

The exponential growth of data in contemporary digital ecosystems necessitates the development of efficient query processing techniques capable of handling large-scale datasets. This paper explores the integration of Artificial Intelligence (AI) methodologies to enhance query processing performance in Big Data environments. By leveraging machine learning algorithms, particularly deep learning models, this study introduces a novel framework that dynamically optimizes query execution plans based on historical data access patterns and workload characteristics. The proposed framework not only minimizes query response times but also improves resource utilization across distributed database systems. Experimental results demonstrate that the AI-driven approach achieves up to 40% reduction in average query response times compared to traditional methods. Furthermore, the scalability of the system is validated through comprehensive testing across various datasets, revealing its capability to adapt to fluctuating data volumes and query complexities. This research contributes to the growing body of knowledge on AI applications in database management, offering insights into the future of scalable query processing in Big Data contexts.