AI-Driven Data Indexing Techniques for Accelerated Retrieval in Cloud Databases

Abstract

In the era of big data, the demand for efficient data retrieval in cloud databases has become increasingly critical. This paper explores AI-driven data indexing techniques designed to enhance retrieval speed and accuracy in cloud-based environments. By leveraging machine learning algorithms and advanced indexing structures, this research proposes a framework that optimizes data organization, facilitates quick access, and improves query performance. The study examines various AI models, including supervised and unsupervised learning approaches, to identify the most effective indexing strategies. Experimental results demonstrate significant improvements in retrieval times and overall system performance compared to traditional indexing methods. This work contributes to the ongoing evolution of cloud database management by presenting innovative solutions that not only address existing challenges but also prepare for future data handling needs.