REPOSE: Distributed Top-k Trajectory Similarity Search with Local Reference Point Tries

TL;DR

REPOSE introduces a distributed in-memory framework with a novel index and partitioning strategy for efficient top-k trajectory similarity search on Spark, outperforming existing methods in real-world scenarios.

Contribution

The paper presents REPOSE, a new distributed framework with RP-Trie indexing and a heterogeneous partitioning strategy for scalable trajectory similarity search.

Findings

Outperforms state-of-the-art proposals in experiments

Effectively handles large-scale real-world trajectory data

Reduces load imbalance in distributed settings

Abstract

Trajectory similarity computation is a fundamental component in a variety of real-world applications, such as ridesharing, road planning, and transportation optimization. Recent advances in mobile devices have enabled an unprecedented increase in the amount of available trajectory data such that efficient query processing can no longer be supported by a single machine. As a result, means of performing distributed in-memory trajectory similarity search are called for. However, existing distributed proposals suffer from either computing resource waste or are unable to support the range of similarity measures that are being used. We propose a distributed in-memory management framework called REPOSE for processing top-k trajectory similarity queries on Spark. We develop a reference point trie (RP-Trie) index to organize trajectory data for local search. In addition, we design a novel heterogeneous global partitioning strategy to eliminate load imbalance in distributed settings. We report on extensive experiments with real-world data that offer insight into the performance of the solution, and show that the solution is capable of outperforming the state-of-the-art proposals.