BRkNN-light: Batch Processing of Reverse k-Nearest Neighbor Queries for Moving Objects on Road Networks

2025-12-29

AI Summary

To address the inefficiency caused by redundant computations in concurrent reverse k-nearest neighbor (RkNN) queries over road networks, this paper proposes the first batch-processing framework for RkNN queries tailored to road networks. Methodologically, it introduces a geometry-constrained pruning and verification mechanism to significantly reduce the search space; employs a dynamic distance cache to enable cross-query reuse of distance computations; and integrates a batch-wise collaborative optimization strategy to improve overall throughput. Extensive experiments on multiple real-world road network datasets demonstrate that the proposed approach reduces total computational cost by up to several-fold compared to state-of-the-art single-query methods, while substantially decreasing query latency. This work achieves, for the first time, efficient and scalable batch RkNN processing in road-network environments.

Technology Category

• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

The Reverse $k$-Nearest Neighbor (R$k$NN) 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 R$k$NN 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 R$k$NN queries, aiming to minimize total computation by sharing duplicate calculations across queries. To tackle this issue, we propose the BR$k$NN-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 R$k$NNs 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 BR$k$NN-Light algorithm on the processing of batch queries.

Problem

• Efficiently processes multiple simultaneous reverse k-nearest neighbor queries
• Minimizes computation by sharing redundant calculations across queries
• Optimizes batch query processing on road networks for moving objects

Innovation

• Batch processing of multiple RkNN queries simultaneously
• Geometric constraints for rapid verification and pruning
• Dynamic distance caching for computation reuse across queries