Strategies for Improving Vector Database Performance through Algorithm Optimization

Zhongqi Zhu

doi:10.54691/eacstn55

Authors

Zhongqi Zhu

DOI:

https://doi.org/10.54691/eacstn55

Keywords:

Vector Database; Algorithm Optimization; Query Efficiency; High Dimensional Data.

Abstract

In the context of the big data era, vector databases play an important role in processing large-scale and complex data. This article explores how to enhance the performance of vector databases through algorithm improvements. Focus on optimizing algorithm adaptability, improving index structure, introducing multi-level indexing and parallel processing technology, and enhancing the scalability of high-dimensional data processing. By deeply analyzing the current technological situation and proposing targeted optimization measures, a complete set of optimization solutions is aimed at improving database retrieval efficiency and data processing capabilities. These strategies can effectively address the challenges in high-dimensional data processing, improve the overall performance of databases, and provide more efficient support for big data application scenarios.

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