CoTra system by Zhi et al. for efficient distributed vector search using RDMA. Published in SIGMOD 2026 proceedings.

SIGKDD 2022 paper introducing BLISS, a billion-scale indexing method using iterative re-partitioning for large-scale approximate nearest neighbor search.

TKDE 2024 paper presenting DIMS, a distributed indexing method for efficient similarity search across metric spaces. The approach enables parallel processing of vector similarity queries at scale.

VLDB 2023 paper presenting ELPIS, a graph-based similarity search approach that combines graph indexing with learning-based techniques for scalable data science applications on large datasets.

A distributed, disk-based vector search system designed for high-throughput approximate nearest neighbor queries at scale. BatANN provides an architecture and methods applicable to large-scale vector databases that need efficient storage beyond memory, enabling cost-effective approximate nearest neighbor search for high-dimensional embeddings.

An efficient vector search system designed for disaggregated memory architectures. d-HNSW optimizes HNSW for environments where compute and memory are separated, typical in modern cloud and distributed systems.