NN Search

Nearest neighbor search (NN search) aims to efficiently find the data points closest to a given query point in a large dataset. Current research focuses on improving the speed and accuracy of NN search, particularly using advanced indexing structures like trees and graph-based methods, and leveraging cross-encoder models for more accurate similarity estimations. These improvements are crucial for handling the ever-increasing volume of data in applications like IoT and machine translation, where efficient retrieval is essential for performance and scalability. Furthermore, theoretical analysis is underway to better understand the trade-offs between approximation accuracy and computational efficiency in various NN search algorithms.