BRkNN-light: Batch Processing of Reverse k-Nearest Neighbor Queries for Moving Objects on Road Networks
Year: 2025
Type: conference-paper
Abstract: The Reverse 𝑘-Nearest Neighbor (R𝑘NN) query over moving objects on road networks seeks to find all moving objects that consider the specified query point as one of their 𝑘 nearest neighbors.In location based services, many users probably submit R𝑘NN queries simultaneously.However, existing methods largely overlook how to efficiently process multiple such queries together, missing opportunities to share redundant computations and thus reduce overall processing costs.To address this, this work is the first to explore batch processing of multiple R𝑘NN queries, aiming to minimize total computation by sharing duplicate calculations across queries.To tackle this issue, we propose the BR𝑘NN-Light algorithm, which uses rapid verification and pruning strategies based on geometric constraints, along with an optimized range search technique, to speed up the process of identifying the R𝑘NNs for each query.Furthermore, it proposes a dynamic distance caching mechanism to enable computation reuse when handling multiple queries, thereby significantly reducing unnecessary computations.Experiments on multiple real-world road networks demonstrate the superiority of the BR𝑘NN-Light algorithm on the processing of batch queries. CCS Concepts• Theory of computation → Database query processing and optimization (theory); Data structures and algorithms for data management.
Authors: Anbang Song, Ziqiang Yu, Wei Liu, Yating Xu, Mingjin Tao
Institution: Yantai University
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G5377955335
OpenAlex ID: W4415149390