Survey of vector database management systems

JJ Pan, J Wang, G Li - The VLDB Journal, 2024‏ - Springer
There are now over 20 commercial vector database management systems (VDBMSs), all
produced within the past five years. But embedding-based retrieval has been studied for …

Oblivious key-value stores and amplification for private set intersection

G Garimella, B Pinkas, M Rosulek, N Trieu… - Advances in Cryptology …, 2021‏ - Springer
Many recent private set intersection (PSI) protocols encode input sets as polynomials. We
consider the more general notion of an oblivious key-value store (OKVS), which is a data …

A survey on learning to hash

J Wang, T Zhang, N Sebe… - IEEE transactions on …, 2017‏ - ieeexplore.ieee.org
Nearest neighbor search is a problem of finding the data points from the database such that
the distances from them to the query point are the smallest. Learning to hash is one of the …

A survey on deep hashing methods

X Luo, H Wang, D Wu, C Chen, M Deng… - ACM Transactions on …, 2023‏ - dl.acm.org
Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …

Practical and optimal LSH for angular distance

A Andoni, P Indyk, T Laarhoven… - Advances in neural …, 2015‏ - proceedings.neurips.cc
We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance
that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal …

New directions in nearest neighbor searching with applications to lattice sieving

A Becker, L Ducas, N Gama, T Laarhoven - … of the twenty-seventh annual ACM …, 2016‏ - SIAM
To solve the approximate nearest neighbor search problem (NNS) on the sphere, we
propose a method using locality-sensitive filters (LSF), with the property that nearby vectors …

Hashing for similarity search: A survey

J Wang, HT Shen, J Song, J Ji - arxiv preprint arxiv:1408.2927, 2014‏ - arxiv.org
Similarity search (nearest neighbor search) is a problem of pursuing the data items whose
distances to a query item are the smallest from a large database. Various methods have …

Optimal data-dependent hashing for approximate near neighbors

A Andoni, I Razenshteyn - Proceedings of the forty-seventh annual ACM …, 2015‏ - dl.acm.org
We show an optimal data-dependent hashing scheme for the approximate near neighbor
problem. For an n-point dataset in a d-dimensional space our data structure achieves query …

[PDF][PDF] Approximate nearest neighbor: Towards removing the curse of dimensionality

S Har-Peled, P Indyk, R Motwani - 2012‏ - dspace.mit.edu
Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality Page 1
Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality The MIT …

Explaining the success of nearest neighbor methods in prediction

GH Chen, D Shah - Foundations and Trends® in Machine …, 2018‏ - nowpublishers.com
Many modern methods for prediction leverage nearest neighbor search to find past training
examples most similar to a test example, an idea that dates back in text to at least the 11th …