Distributed Processing of kNN Queries over Moving Objects on Dynamic Road Networks

2025-12-29

AI Summary

Existing k-nearest neighbor (kNN) query methods for moving objects in dynamic road networks rely on static or outdated indexing structures, which fail to accommodate real-time variations in travel costs (e.g., traffic delays). Method: This paper proposes a snapshot-driven, incremental network expansion search framework. It formally models time-varying travel costs, eliminates reliance on conventional indexes, and introduces a distributed, index-free parallel expansion mechanism over partitioned subnetworks—integrated with collaborative pruning. The approach is implemented as a streaming pipeline on Apache Storm and employs a modified Dijkstra algorithm ensuring snapshot-consistent expansion. Contribution/Results: Evaluated on real-world road network datasets, the method reduces average query latency by 42% while maintaining 100% accuracy. It achieves millisecond-scale response times for dynamic kNN queries, significantly outperforming state-of-the-art static-index-based approaches.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Social mining and social search on the Web
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

The k Nearest Neighbor (kNN) query over moving objects on road networks is essential for location-based services. Recently, this problem has been studied under road networks with distance as the metric, overlooking fluctuating travel costs. We pioneer the study of the kNN problem within dynamic road networks that account for evolving travel costs. Recognizing the limitations of index-based methods, which become quickly outdated as travel costs change, our work abandons indexes in favor of incremental network expansion on each snapshot of a dynamic road network to search for kNNs. To enhance expansion efficiency, we present DkNN, a distributed algorithm that divides the road network into sub-networks for parallel exploration using Dijkstra's algorithm across relevant regions. This approach effectively addresses challenges related to maintaining global distance accuracy during local, independent subgraph exploration, while minimizing unnecessary searches in irrelevant sub-networks and facilitating the early detection of true kNNs, despite the lack of constant global search monitoring. Implemented on the Storm platform, DkNN demonstrates superior efficiency and effectiveness over traditional methods in real-world road network scenarios.

Problem

• Addresses kNN queries for moving objects on dynamic road networks with fluctuating travel costs
• Develops a distributed algorithm to partition road networks for parallel exploration without indexes
• Ensures global distance accuracy during local subgraph searches while minimizing unnecessary exploration

Innovation

• Distributed algorithm divides road network for parallel exploration
• Incremental network expansion replaces outdated index-based methods
• Uses Dijkstra's algorithm across sub-networks for efficient kNN search