Traditional database management systems need help efficiently represent and querying the complex, high-dimensional data prevalent in modern applications. Vector databases offer a solution by storing data as numerical vectors within a multi-dimensional space. This enables similarity-based search and analysis, such as image retrieval, recommendation engine generation, and natural language processing. This paper introduces Quantixar, a vector database project designed for efficiency in high-dimensional settings. Quantixar tackles the challenge of managing high-dimensional data by strategically combining advanced indexing and quantization techniques. It employs HNSW indexing for accelerated ANN search. Additionally, Quantixar incorporates binary and product quantization to compress high-dimensional vectors, reducing storage requirements and computational costs during search. The paper delves into Quantixar's architecture, specific implementation, and experimental methodology.
@article{arxiv.2403.12583,
title = {Quantixar: High-performance Vector Data Management System},
author = {Gulshan Yadav and RahulKumar Yadav and Mansi Viramgama and Mayank Viramgama and Apeksha Mohite},
journal= {arXiv preprint arXiv:2403.12583},
year = {2024}
}