4 research outputs found
Efficient Non-Learning Similar Subtrajectory Search
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.Comment: VLDB 202