Updatable Balanced Index for Fast On-device Search with Auto-selection Model
Yushuai Ji, Sheng Wang, Zhiyu Chen, Yuan Sun, Zhiyong Peng

TL;DR
This paper introduces UnIS, an improved index structure for on-device search that accelerates index construction, insertion, and query performance through dataset-aware strategies and auto-selected search methods, significantly outperforming previous approaches.
Contribution
UnIS presents a novel, dataset-aware approach to accelerate BMKD-tree construction, implement efficient rebalancing, and automatically optimize search strategies for on-device data retrieval.
Findings
17.96x faster index construction
1.60x faster data insertion
7.15x faster kNN search
Abstract
Diverse types of edge data, such as 2D geo-locations and 3D point clouds, are collected by sensors like lidar and GPS receivers on edge devices. On-device searches, such as k-nearest neighbor (kNN) search and radius search, are commonly used to enable fast analytics and learning technologies, such as k-means dataset simplification using kNN. To maintain high search efficiency, a representative approach is to utilize a balanced multi-way KD-tree (BMKD-tree). However, the index has shown limited gains, mainly due to substantial construction overhead, inflexibility to real-time insertion, and inconsistent query performance. In this paper, we propose UnIS to address the above limitations. We first accelerate the construction process of the BMKD-tree by utilizing the dataset distribution to predict the splitting hyperplanes. To make the continuously generated data searchable, we propose a…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · Advanced Neural Network Applications
