Survey of Vector Database Management Systems
A comprehensive 2023 survey that systematically analyzes the design, architecture, indexing techniques, and system implementations of modern vector database management systems, serving as a foundational reference for understanding the vector database ecosystem used in AI applications.
About this tool
Survey of Vector Database Management Systems
Type: Academic paper / survey (2023)
Category: Curated Resource Lists
Source: arXiv:2310.14021
Length: 25 pages
Discipline: Computer Science – Databases (cs.DB)
Overview
“Survey of Vector Database Management Systems” is a 25‑page academic survey that analyzes the design and architecture of modern vector database management systems. It focuses on how these systems store, index, and serve high-dimensional vector data, particularly for AI and machine learning applications.
Features
- Comprehensive 2023 survey of vector database management systems and their ecosystem.
- Systematic analysis of design and architecture, including how vector DBMSs are structured and how they differ from traditional databases.
- Coverage of indexing techniques for high-dimensional vectors (e.g., ANN and related vector search methods).
- Discussion of system implementations, describing how modern vector databases are built in practice.
- Positioning within AI workflows, focusing on how vector DBMSs support AI and ML applications that rely on vector search and embeddings.
- Foundational reference for understanding the broader vector database ecosystem, suitable for researchers, practitioners, and engineers working with vector data.
Access
- ArXiv record: https://arxiv.org/abs/2310.14021
- DOI: https://doi.org/10.48550/arXiv.2310.14021
Pricing
- Access via arXiv is typically free to read and download (no listed pricing on the source page).
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