Analysis of Efficient Database Indexing Strategies for Machine Learning

Abstract

With the wide application of machine learning in various fields, the performance of databases, as the core of data storage and management, has a significant impact on the efficiency of machine learning tasks. An efficient database indexing strategy can accelerate data retrieval, reduce the data acquisition time of machine learning algorithms, and thereby enhance the overall training and inference speed. This paper conducts an in-depth analysis of the database indexing requirements for machine learning, explores the limitations of traditional indexing strategies in machine learning scenarios, elaborates in detail several efficient indexing strategies suitable for machine learning, including feature similarity-based indexing, learning model-based indexing, and multi-dimensional data indexing, etc. Finally, it looks forward to future research directions.