GeoSOT-Based Spatiotemporal Index of Massive Trajectory Data

Chunyao Qian,Chengqi Cheng,Guoliang Pu,Huangchuang Zhang,Chao Yi,Xiaofeng Wei

doi:10.3390/ijgi8060284

Chunyao Qian, Chengqi Cheng + Show 4 more

Open Access

https://doi.org/10.3390/ijgi8060284

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Abstract

With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS (location based service) applications have provided us with trajectory data services such as traffic flow statistics and user behavior pattern analyses. However, the storage and query efficiency of massive trajectory data are increasingly creating a bottleneck for these applications, especially for large-scale spatiotemporal query scenarios. To solve this problem, we propose a new spatiotemporal indexing method to improve the query efficiency of massive trajectory data. First, the method extends the GeoSOT spatial partitioning scheme to the time dimension and forms a global space–time subdivision scheme. Second, a novel multilevel spatiotemporal grid index, called the GeoSOT ST-index, was constructed to organize trajectory data hierarchically. Finally, a spatiotemporal range query processing method is proposed based on the index. We implement and evaluate the index in MongoDB. By comparing the range query efficiency and scalability of our index with those of the other two space–time composite indexes, we found that our approach improves query efficiency levels by approximately 40% and has better scalability under different data volumes.

Highlights

With the development of sensor technologies and with the pervasiveness of intelligent mobile terminals, movements of humans and objects are increasingly being recorded as trajectories
One must access different samples of trajectories or different parts of a trajectory many times. This stage is referred to as trajectory data indexing and retrieving [5], and it constitutes a fundamental step of many trajectory data mining tasks
The problem is that the large amount of data leads to long query periods, which reduces the efficiency at which applications can mine data and provide corresponding services, especially for online applications requiring the instant mining of trajectory data

Summary

Introduction

With the development of sensor technologies and with the pervasiveness of intelligent mobile terminals, movements of humans and objects are increasingly being recorded as trajectories. A broad range of LBS (location-based service) applications have been developed and improved through trajectory data mining [1], such as traffic flow statistical analysis [2], vehicle scheduling strategies [3], movement pattern analysis [4] for individuals or groups of moving objects, making sense of trajectories, and other applications of urban services. These applications significantly benefit common individuals, traffic organizations, and government agencies.

Related Work

Findings

GeoSOT-Based Spatiotemporal Index Model