The ML-based inferential statistics framework presents a novel solution for database query optimization that addresses critical challenges in statistics maintenance and cardinality estimation. By combining Bayesian learning and reinforcement learning modules, the framework enables continuous adaptation to changing data patterns while minimizing computational overhead. The solution offers improved query performance through better plan selection, reduced resource consumption, and enhanced accuracy in cardinality estimation. The framework’s dynamic histogram redistribution mechanism ensures optimal statistics maintenance in high-throughput environments, making it particularly effective for enterprise-scale databases with rapidly evolving data distributions.
Keywords: adaptive histograms, cardinality estimation, database performance, machine learning statistics, query optimization
