This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory
approximate nearest neighbor algorithms. It provides a standard interface for measuring the …

This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory
approximate nearest neighbor algorithms. It provides a standard interface for measuring the …

This article reviews index structures for fast similarity search for objects represented by
binary vectors (with components equal to 0 or 1). Structures for both exact and approximate …

The k-nearest-neighbors (k NN) graph is a popular and powerful data structure that is used
in various areas of Data Science, but the high computational cost of obtaining it hinders its …

This survey paper considers index structures for fast similarity search for objects represented
by real-valued vectors. Index structures based on locality-sensitive hashing and their …

Cardinality estimation is perhaps the simplest non-trivial statistical problem that can be
solved via sketching. Industrially-deployed sketches like HyperLogLog, MinHash, and PCSA …

We study sketching schemes for the cardinality estimation problem in data streams, and
advocate for measuring the efficiency of such a scheme in terms of its MVP: Memory …

Similarity Search: Algorithms for Sets and other High Dimensional Data Page 1 Similarity
Search: Algorithms for Sets and other High Dimensional Data Thomas Dybdahl Ahle Advisor …

Дан обзор индексных структур для быстрого поиска по сходству объектов,
представленных вещественными векторами. Рассмотрены структуры как для точного …

Дан обзор индексных структур для быстрого поиска по сходr ству объектов,
представленных бинарными векторами (с компонентами 0 или 1). Рассмотрены …