Juno — Optimizing ANNS with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping

SIGMOD 2025 paper proposing optimizations for graph-based approximate nearest neighbor search indexing on modern CPU architectures, leveraging SIMD instructions and cache-aware algorithms for improved index construction performance.

VLDB 2025 paper proposing efficient level navigation with shortcuts for accelerating approximate nearest neighbor search in hierarchical graph indexes, improving traversal speed across multi-layer graph structures.

SIGKDD 2025 paper proposing adaptive awareness capabilities for graph-based approximate nearest neighbor search, enabling the search algorithm to dynamically adjust its strategy based on local graph characteristics and query properties.

ACORN is a performant and predicate-agnostic search system for vector embeddings and structured data, enhancing the capability of vector databases to handle complex queries over high-dimensional data efficiently.

This survey paper provides an overview of the landscape, technologies, and applications of vector databases, making it a valuable resource for understanding the field.

A comprehensive academic survey that explores the architecture, storage, retrieval techniques, and challenges associated with vector databases. It categorizes algorithmic approaches to approximate nearest neighbor search (ANNS) and discusses how vector databases can be integrated with large language models, offering valuable insights and foundational knowledge for understanding and building vector database systems.