Work

PANNS: Enhancing Graph-based Approximate Nearest Neighbor Search through Recency-aware Construction and Parameterized Search

Year: 2025

Type: conference-paper

Abstract: Approximate Nearest-Neighbor Search (ANNS) has become the standard querying method in vector databases, especially with the recent surge in large-scale, high-dimensional data driven by LLM-based applications. Recently, graph-based ANNS has shown improved throughput by constructing a graph from the dataset, with edges representing the distances between data points, and using best-first or beam search algorithms for query evaluation.

Source: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

DOI: 10.1145/3710848.3710867

Authors: Xizhe Yin, Chao Gao, Zhijia Zhao, Rajiv Gupta

Institution: University of California, Riverside

Language: English

Topic: Advanced Image and Video Retrieval Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Universitas Brawijaya

Award: G2697103953