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DeWu Technology
DeWu Technology
Jul 27, 2022 · Artificial Intelligence

Overview of Nearest Neighbor Search Algorithms

The article reviews how high‑dimensional vector representations in deep‑learning applications require efficient approximate nearest‑neighbor search, comparing K‑d trees, hierarchical k‑means trees, locality‑sensitive hashing, product quantization, and HNSW graphs, and discusses practical FAISS implementations and how algorithm choice depends on data size, recall, latency, and resources.

HNSWKD-TreeLSH
0 likes · 8 min read
Overview of Nearest Neighbor Search Algorithms
Architecture Digest
Architecture Digest
Jun 24, 2022 · Backend Development

Evolution and Optimization of Numeric Indexing for Geolocation in Elasticsearch

This article reviews the evolution and optimization of Elasticsearch's numeric indexing for geolocation from 2015 to present, covering early string-based methods, KD‑Tree, Quadtree, and BKD‑tree implementations, and explains how these advances enable millisecond‑level POI searches using geo_distance queries.

BKD-TreeElasticsearchKD-Tree
0 likes · 22 min read
Evolution and Optimization of Numeric Indexing for Geolocation in Elasticsearch
vivo Internet Technology
vivo Internet Technology
Jun 22, 2022 · Big Data

Evolution and Optimization of Numerical Indexing in Elasticsearch for Geo‑Location Queries

The article traces Elasticsearch’s geo‑indexing evolution from early string‑based simulations through Quadtree filters to the modern BKD‑tree implementation, showing how each optimization dramatically improves memory usage, query speed, and accuracy for large‑scale point‑of‑interest searches in location‑based services.

BKD-TreeElasticsearchGeo-Location
0 likes · 25 min read
Evolution and Optimization of Numerical Indexing in Elasticsearch for Geo‑Location Queries
IEG Growth Platform Technology Team
IEG Growth Platform Technology Team
Jan 17, 2022 · Artificial Intelligence

Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms

This article introduces the concept of vector retrieval, outlines its diverse application scenarios, explains common distance metrics for both floating‑point and binary vectors, and surveys fundamental approximate nearest‑neighbor algorithms including tree‑based, graph‑based, quantization, and hashing methods.

HNSWKD-TreeLSH
0 likes · 22 min read
Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms
vivo Internet Technology
vivo Internet Technology
Nov 16, 2018 · Artificial Intelligence

Efficient Vector Search with Deep Learning Embeddings in Elasticsearch

The article explains how to replace keyword matching with deep‑learning document embeddings in Elasticsearch by applying PCA dimensionality reduction, indexing vectors using Lucene’s KD‑tree structures via a custom plugin, and leveraging FAISS‑style nearest‑neighbour techniques to achieve fast, semantically aware similarity search.

Deep LearningElasticsearchKD-Tree
0 likes · 7 min read
Efficient Vector Search with Deep Learning Embeddings in Elasticsearch