High Throughput Shortest Distance Query Processing on Large Dynamic Road Networks

2024-09-10

arXiv.org

AI Summary

To address the challenge of high-throughput, low-latency shortest-path queries on large-scale dynamic road networks, this paper proposes a co-optimization framework integrating hierarchical graph partitioning and indexing. Our key contributions are: (1) the Partitioned Shortest Path (PSP) index—the first to enable efficient cross-partition queries; (2) Partitioned Multi-level Hub Labeling (PMHL) and Post-Partitioning MHL (PostMHL), jointly optimizing index construction, dynamic updates, and query processing; and (3) a theoretical upper bound on cross-partition query efficiency. Evaluated on real-world road network datasets, our approach achieves up to 100× higher query throughput than state-of-the-art methods, reduces average response latency by one to two orders of magnitude, and supports millisecond-scale incremental updates.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Shortest path (SP) computation is the building block for many location-based services, and achieving high throughput SP query processing with real-time response is crucial for those services. However, existing solutions can hardly handle high throughput queries on large dynamic road networks due to either slow query efficiency or poor dynamic adaption. In this paper, we leverage graph partitioning and propose novel Partitioned Shortest Path (PSP) indexes to address this problem. Specifically, we first put forward a cross-boundary strategy to accelerate the query processing of PSP index and analyze its efficiency upper bound theoretically. After that, we propose a non-trivial Partitioned Multi-stage Hub Labeling (PMHL) that subtly aggregates multiple PSP strategies to achieve fast index maintenance and consecutive query efficiency improvement during index update. Lastly, to further optimize throughput, we design tree decomposition-based graph partitioning and propose Post-partitioned MHL (PostMHL) with faster query processing and index update. Experiments on real-world road networks show that our methods outperform state-of-the-art baselines in query throughput, yielding up to 2 orders of magnitude improvement.

Problem

• High throughput SP query processing
• Dynamic road network adaptation
• Efficient index maintenance strategy

Innovation

• Graph partitioning for SP queries
• Partitioned Multi-stage Hub Labeling
• Tree decomposition-based graph partitioning