Vector database management systems: Fundamental concepts, use-cases, and current challenges

Abstract

Vector database management systems have emerged as an important component in modern data management, driven by the growing importance for the need to computationally describe rich data such as texts, images and video in various domains such as recommender systems, similarity search, and chatbots. These data descriptions are captured as numerical vectors that are computationally inexpensive to store and compare. However, the unique characteristics of vectorized data, including high dimensionality and sparsity, demand specialized solutions for efficient storage, retrieval, and processing. This narrative literature review provides an accessible introduction to the fundamental concepts, use-cases, and current challenges associated with vector database management systems, offering an overview for researchers and practitioners seeking to facilitate effective vector data management.

arXiv comment13 pages, 5 figures

References in corpus (6)

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Long Range Arena: A Benchmark for Efficient Transformers
• Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2
• How Language Model Hallucinations Can Snowball
• The Universal NFT Vector Database: A Scaleable Vector Database for NFT Similarity Matching
• Thistle: A Vector Database in Rust

Cited by in corpus (1)

• Encapsulating Textual Contents into a MOC data Structure for Advanced Applications