A Brief Survey of Vector Databases

ArXiv 2023 survey paper categorizing ANN algorithms (hash/tree/graph/quantization) for vector databases, covering architecture, storage, retrieval, and LLM integration. Details benchmarks reviewed and accuracy-scalability trade-offs. Suited for academic/research use in ANN method selection; contrasts 2023 algorithmic depth with prior system surveys and 2026 benchmarks.

VLDB 2024 survey paper on vector DB management systems, detailing ANN indexing (graph/tree/hash/quantization), architectures, query processing. Reviews benchmarks and scaling challenges. Key for academic/research literature; compares full-system 2024 analysis with prior surveys and 2026 benchmarks.

Cognitive Systems 2024 survey paper on VDBMS fundamentals, ANN indexing, use cases, challenges. Reviews benchmarks in high-dim data; notes theory/practice gaps. Academic/research essential; contrasts conceptual 2024 view with prior and 2026 practical benchmarks.

arXiv 2024 research paper investigating ANN search performance on dynamic datasets with updates. Reviews benchmarks for vector indexing adaptability and efficiency. For academic/research use in dynamic vector DB scenarios; compares to prior static benchmarks and 2026 dynamic trends.

arXiv 2024 research paper proposing learned cluster representatives for efficient ANN search via vector quantization and clustering. Reviews benchmarks for scalability in similarity search. Academic/research use for advanced indexing techniques; contrasts with prior methods and 2026 learned index trends.

arXiv 2024 research paper providing practical guidance on HNSW, flat, and inverted indexes for dense/sparse retrieval in vector systems. Reviews performance benchmarks across retrievers. For research/academic optimization of AI retrieval; compares index choices vs 2026 hybrid trends.