DIMS — Distributed Index for Similarity Search in Metric Spaces

VLDB 2023 paper introducing an adaptive indexing approach for high-dimensional metric spaces that dynamically adjusts its structure based on query workloads to improve search performance over time.

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

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.

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.

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.