Exploring AI-Based Methods for Efficient Database Index Compression

Abstract

With the exponential growth of data, the demand for efficient database management has never been greater. One of the most critical challenges in this domain is reducing the storage overhead associated with database indices, which are essential for accelerating query performance. Traditional index compression techniques, while effective, often face limitations in terms of scalability, speed, and adaptability to dynamic workloads. This paper explores the application of artificial intelligence (AI)-based methods for optimizing database index compression. By leveraging machine learning algorithms, deep neural networks, and reinforcement learning, AI can significantly enhance the compression ratio while maintaining or even improving query performance. The proposed methods adaptively adjust to changes in database workloads, improving both storage efficiency and access speed. We also introduce a hybrid AI approach that combines model-driven and heuristic techniques to address specific challenges like index fragmentation and retrieval time. Experimental evaluations on real-world databases demonstrate the effectiveness of the AI-based methods in achieving significant storage savings without compromising performance. This work paves the way for more intelligent and adaptive solutions in database management, contributing to the broader goal of making databases more efficient in the era of big data