Vector database management systems: Fundamental concepts, use-cases, and current challenges
Системы управления векторными базами данных: фундаментальные концепции, сценарии использования и текущие проблемы
2024-02-15
SCID: 54.1/y2hgqjga
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Abstract (AI)
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.
Key Findings
1
Numerical vectors are computationally inexpensive to store and compare, making them practical for large-scale applications.
2
There exist current challenges in efficient vector data management that motivate further research and system design improvements.
3
This work provides a narrative literature review that synthesizes fundamental concepts, use-cases, and challenges for researchers and practitioners.
4
Vector DBMSs are essential for managing numerical vector representations of rich data (text, images, video) used in recommender systems, similarity search, and chatbots.
5
Vectorized data characteristics—high dimensionality and sparsity—require specialized storage, retrieval, and processing solutions.
Research Object
Vector database management systems
Research Subject
Fundamental concepts, use-cases, and current challenges for efficient storage, retrieval, and processing of high-dimensional sparse vectorized data
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Publication Date
2024-02-15
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