Efficient Non-Learning Similar Subtrajectory Search

TL;DR

This paper introduces an exact algorithm for similar subtrajectory search that operates in linear time relative to trajectory lengths, significantly improving efficiency over previous methods while maintaining accuracy.

Contribution

The paper presents the first exact linear-time algorithm for similar subtrajectory search applicable to many common trajectory distance functions.

Findings

The proposed algorithm runs in O(mn) time, faster than previous O(mn^2) algorithms.

Extensive experiments confirm the efficiency and effectiveness of the new method.

The algorithm is applicable to various distance functions like WED, DTW, ERP, EDR, and Frechet.

Abstract

Similar subtrajectory search is a finer-grained operator that can better capture the similarities between one query trajectory and a portion of a data trajectory than the traditional similar trajectory search, which requires the two checked trajectories are similar to each other in whole. Many real applications (e.g., trajectory clustering and trajectory join) utilize similar subtrajectory search as a basic operator. It is considered that the time complexity is O(mn^2) for exact algorithms to solve the similar subtrajectory search problem under most trajectory distance functions in the existing studies, where m is the length of the query trajectory and n is the length of the data trajectory. In this paper, to the best of our knowledge, we are the first to propose an exact algorithm to solve the similar subtrajectory search problem in O(mn) time for most of widely used trajectory distance functions (e.g., WED, DTW, ERP, EDR and Frechet distance). Through extensive experiments on three real datasets, we demonstrate the efficiency and effectiveness of our proposed algorithms.