Neural Network Powered Indexing Techniques for High Performance Data Retrieval

Abstract

Modern applications’ data growth poses the limitation of requiring highly effective and flexible indexing methods. Structures such as B-tree, hash index, and spatial tree are considered the oldest forms of indexing, but they seem to fail when it comes to high and constantly changing dimensional structures. The objective of this paper is to increase the speed, precision, and scope of data retrieval enhancement using the indexing methods powered by neural networks. We teach neural networks that cleverly change the key space to storage space as a mapping function to minimize query time and enhance retrieval accuracy. The proposed framework has been tested thoroughly against learned index and classical index baselines on several datasets and workloads. The findings are substantial concerning response times greatly improving with lower memory use and tracking more data changes. Besides filling the gap between deep learning and systems optimization, this paper marks the first step towards building intelligent indexing systems. Deep learning coupled with fast changing data environments challenge the future of indexing systems, and this document is the groundwork for such advanced systems.