AI-Enabled Partitioning of Large-Scale JSON Data for Enhanced Processing Performance

Managing extremely large JSON datasets introduces significant challenges related to scalability, processing efficiency, and query performance. Traditional partitioning methods often struggle with the complexity and irregular structure of JSON data, leading to bottlenecks in system performance. This research introduces an AI-driven partitioning approach that intelligently divides massive JSON files by analyzing data patterns, usage frequency, and hierarchical structures. Machine learning models are employed to recommend optimal partitioning strategies in real time, ensuring efficient resource utilization and balanced data distribution. Experimental analysis on real-world datasets indicates that the proposed method enhances processing speeds, lowers query execution times, and improves system throughput compared to conventional techniques. Results show up to a 35% decrease in query latency and a 40% gain in data access efficiency. This study demonstrates that integrating AI techniques with big data partitioning significantly optimizes the handling of semi-structured formats like JSON, offering a robust foundation for future intelligent data management systems.