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Quantixar: High-performance Vector Data Management System

Gulshan Yadav, RahulKumar Yadav, Mansi Viramgama, Mayank Viramgama, Apeksha Mohite

TL;DR

This paper introduces Quantixar, a vector database project designed for efficiency in high-dimensional settings that employs HNSW indexing for accelerated ANN search and incorporates binary and product quantization to compress high-dimensional vectors, reducing storage requirements and computational costs during search.

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.

Quantixar: High-performance Vector Data Management System

TL;DR

This paper introduces Quantixar, a vector database project designed for efficiency in high-dimensional settings that employs HNSW indexing for accelerated ANN search and incorporates binary and product quantization to compress high-dimensional vectors, reducing storage requirements and computational costs during search.

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.
Paper Structure (16 sections, 1 figure, 1 table)

This paper contains 16 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Curse of Dimensionality
  4. Theoretical Underpinning
  5. Implications for Search Efficiency
  6. Approaches to Address High-Dimensionality Challenges
  7. Hierarchical Navigable Small World (HNSW)
  8. Quantization: Compressing Vectors for Efficiency
  9. Advanced Techniques: Harnessing Hardware with SIMD
  10. Methodology
  11. Query Processing :
  12. Quantization
  13. Indexing
  14. Vector Storage
  15. Results
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Quantixar System Architecture.