Quantifying Point Contributions: A Lightweight Framework for Efficient and Effective Query-Driven Trajectory Simplification

2024-10-01

Proceedings of the VLDB Endowment

AI Summary

Existing trajectory simplification methods suffer from three key limitations: reliance on iterative optimization, neglect of global structural patterns leading to distorted similarity queries, and difficulty in distinguishing the importance of homogeneous points. This paper proposes MLSimp, a query-driven lightweight trajectory simplification framework featuring a novel dual-model collaboration: GNN-TS and Diff-TS. GNN-TS jointly models global relational dependencies and local uniqueness via graph attention, while Diff-TS enhances salient point signals through diffusion-based learning, eliminating iterative refinement. Evaluated on three real-world trajectory datasets against eight state-of-the-art baselines, MLSimp reduces simplification time by 42%–70% and improves similarity query accuracy by up to 34.6%. The framework thus achieves a superior trade-off between compression efficiency and query fidelity.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

As large volumes of trajectory data accumulate, simplifying trajectories to reduce storage and querying costs is increasingly studied. Existing proposals face three main problems. First, they require numerous iterations to decide which GPS points to delete. Second, they focus only on the relationships between neighboring points (local information) while neglecting the overall structure (global information), reducing the global similarity between the simplified and original trajectories and making it difficult to maintain consistency in query results, especially for similarity-based queries. Finally, they fail to differentiate the importance of points with similar features, leading to suboptimal selection of points to retain the original trajectory information. We propose MLSimp, a novel Mutual Learning query-driven trajectory simplification framework that integrates two distinct models: GNN-TS, based on graph neural networks, and Diff-TS, based on diffusion models. GNN-TS evaluates the importance of a point according to its globality, capturing its correlation with the entire trajectory, and its uniqueness, capturing its differences from neighboring points. It also incorporates attention mechanisms in the GNN layers, enabling simultaneous data integration from all points within the same trajectory and refining representations, thus avoiding iterative processes. Diff-TS generates amplified signals to enable the retention of the most important points at low compression rates. Experiments involving eight baselines on three databases show that MLSimp reduces the simplification time by 42%--70% and improves query accuracy over simplified trajectories by up to 34.6%.

Problem

• Reduces storage and querying costs by simplifying trajectory data.
• Improves global similarity and query consistency in trajectory simplification.
• Differentiates point importance to optimize trajectory information retention.

Innovation

• Integrates GNN-TS and Diff-TS models for trajectory simplification.
• Uses attention mechanisms in GNN layers for efficient data integration.
• Generates amplified signals to retain important points effectively.