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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.12583 |
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| _version_ | 1866916166307414016 |
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| author | Yadav, Gulshan Yadav, RahulKumar Viramgama, Mansi Viramgama, Mayank Mohite, Apeksha |
| author_facet | Yadav, Gulshan Yadav, RahulKumar Viramgama, Mansi Viramgama, Mayank Mohite, Apeksha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12583 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantixar: High-performance Vector Data Management System Yadav, Gulshan Yadav, RahulKumar Viramgama, Mansi Viramgama, Mayank Mohite, Apeksha Databases 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. |
| title | Quantixar: High-performance Vector Data Management System |
| topic | Databases |
| url | https://arxiv.org/abs/2403.12583 |