Research paper introducing CAPS, a practical partition index designed for filtered similarity search. Published as an arXiv preprint in 2023 by Gaurav Gupta et al., it addresses the challenge of combining attribute filtering with approximate nearest neighbor search efficiently.

VLDB 2024 paper presenting DIDS, a fast similarity search method using double indices and double summarizations to accelerate high-dimensional vector queries.

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 2022 paper introducing Hercules, an approach for efficient data series (time series) similarity search at scale, leveraging advanced indexing and pruning techniques for billion-scale sequence datasets.

Research paper introducing LANNS, a web-scale approximate nearest neighbor lookup system developed at Facebook (Meta). Published as an arXiv preprint in 2020, it describes techniques for serving ANN search at massive scale in production systems.

NeurIPS 2023 paper presenting NHQ, an efficient and robust framework for approximate nearest neighbor search with attribute constraints, enabling hybrid queries combining vector similarity with structured filtering.