GTRSS: Graph-based Top-$k$ Representative Similar Subtrajectory Query

2025-07-07

AI Summary

This paper addresses the Top-k Representative Similar Subtrajectory Query (TRSSQ) problem by proposing the first graph-structured, efficient solution. To overcome performance bottlenecks inherent in conventional filter-and-verify frameworks, we introduce a novel two-level graph index—the first such application of graph structures to subtrajectory retrieval. Our method integrates a Data Trajectory Similarity Measure (DTSM) with an R-tree–grid hybrid filtering scheme and devises DTSM-based pruning rules to accelerate both index construction and query processing. It enables query-oriented clustering navigation, eliminating exhaustive scans and costly similarity computations. Evaluated on real-world trajectory datasets, our approach achieves over 90% retrieval accuracy and delivers up to two orders of magnitude speedup in query latency compared to state-of-the-art methods, significantly advancing both efficiency and precision in representative subtrajectory search.

Technology Category

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

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
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Trajectory mining has attracted significant attention. This paper addresses the Top-k Representative Similar Subtrajectory Query (TRSSQ) problem, which aims to find the k most representative subtrajectories similar to a query. Existing methods rely on costly filtering-validation frameworks, resulting in slow response times. Addressing this, we propose GTRSS, a novel Graph-based Top-k Representative Similar Subtrajectory Query framework. During the offline phase, GTRSS builds a dual-layer graph index that clusters trajectories containing similar representative subtrajectories. In the online phase, it efficiently retrieves results by navigating the graph toward query-relevant clusters, bypassing full-dataset scanning and heavy computation. To support this, we introduce the Data Trajectory Similarity Metric (DTSM) to measure the most similar subtrajectory pair. We further combine R-tree and grid filtering with DTSM pruning rules to speed up index building. To the best of our knowledge, GTRSS is the first graph-based solution for top-k subtrajectory search. Experiments on real datasets demonstrate that GTRSS significantly enhances both efficiency and accuracy, achieving a retrieval accuracy of over 90 percent and up to two orders of magnitude speedup in query performance.

Problem

• Efficiently find top-k representative similar subtrajectories to a query
• Overcome slow response of existing filtering-validation frameworks
• Propose graph-based indexing for faster and more accurate retrieval

Innovation

• Dual-layer graph index for trajectory clustering
• Data Trajectory Similarity Metric (DTSM)
• Combines R-tree and grid filtering