Enhancing Horizontal Partitioning in DBMS Through Machine Learning-Guided Metaheuristics

Abstract

The exponential growth in data volume presents significant challenges for efficient query processing. Horizontal partitioning is a key optimization technique used to address this issue. However, identifying the optimal partitioning scheme is an NP-hard problem due to the vast solution space. While metaheuristics like NSGA-II have been applied, their effectiveness is often limited by static parameters. This paper proposes a novel hybrid approach that integrates machine learning with metaheuristics to enhance horizontal partitioning. By using ML to generate high-quality initial solutions and dynamically adjust genetic algorithm parameters, the method improves both convergence speed and solution quality. Experimental results demonstrate that this ML-guided metaheuristic approach significantly enhances query performance and overall system efficiency.