Vector Databases
Department of Master of Computer Applications, RV College of Engineering, Bengaluru, India.
Research Article
Open Access Research Journal of Engineering and Technology, 2024, 07(01), 096–104.
Article DOI: 10.53022/oarjet.2024.7.1.0043
Publication history:
Received on 16 July 2024; revised on 04 September 2024; accepted on 06 September 2024
Abstract:
Vector databases represent a significant advancement in data management, tailored to efficiently handle high-dimensional vector embeddings. Unlike traditional databases, which face challenges with the complexity and scale of high-dimensional data, vector databases are engineered for optimal storage, indexing, and retrieval of vector data. This work explores the challenges posed by high-dimensional data, including the curse of dimensionality, and examines how vector databases address these issues through advanced indexing techniques such as Inverted File (IVF), Product Quantization (PQ), and Locality Sensitive Hashing (LSH). It highlights the importance of vector databases in facilitating rapid similarity searches, which are crucial for applications such as recommendation systems and search engines. The discussion also covers the evolution of vector databases and their impact on AI and machine learning.
Keywords:
Vector Databases; High-Dimensional Data; Similarity Search; Inverted File (IVF); Product Quantization (PQ); Locality Sensitive Hashing (LSH).
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0