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arXiv · 2403.12583

Quantixar: High-performance Vector Data Management System

Gulshan Yadav·RahulKumar Yadav·Mansi Viramgama·Mayank Viramgama·Apeksha Mohite
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Abstract

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.

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Gulshan Yadav, RahulKumar Yadav, Mansi Viramgama, Mayank Viramgama, Apeksha Mohite. 2024-03-19. Quantixar: High-performance Vector Data Management System. https://arxiv.org/abs/2403.12583

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