LiMITS: An Effective Approach for Trajectory Simplification

TL;DR

This paper introduces LiMITS, a novel trajectory simplification method using $L_$ metric and multidimensional interpolation, significantly improving data compression and processing efficiency for high-dimensional trajectory data.

Contribution

The paper presents LiMITS, a new trajectory simplification algorithm that extends existing methods with $L_$ metric and introduces a compact representation for better compression.

Findings

LiMITS outperforms existing methods in trajectory simplification.

It achieves higher compression ratios with effective data reduction.

Experimental results validate its efficiency on real-world datasets.

Abstract

Trajectories represent the mobility of moving objects and thus is of great value in data mining applications. However, trajectory data is enormous in volume, so it is expensive to store and process the raw data directly. Trajectories are also redundant so data compression techniques can be applied. In this paper, we propose effective algorithms to simplify trajectories. We first extend existing algorithms by replacing the commonly used $L_2$ metric with the $L_\infty$ metric so that they can be generalized to high dimensional space (e.g., 3-space in practice). Next, we propose a novel approach, namely L-infinity Multidimensional Interpolation Trajectory Simplification (LiMITS). LiMITS belongs to weak simplification and takes advantage of the $L_\infty$ metric. It generates simplified trajectories by multidimensional interpolation. It also allows a new format called compact representation to further improve the compression ratio. Finally, We demonstrate the performance of LiMITS through experiments on real-world datasets, which show that it is more effective than other existing methods.