GeoSOT-Based Spatiotemporal Index of Massive Trajectory Data

• Published in: ISPRS international journal of geo-information Vol. 8; no. 6; p. 284
• Main Authors: Qian, Chunyao, Yi, Chao, Cheng, Chengqi, Pu, Guoliang, Wei, Xiaofeng, Zhang, Huangchuang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2019
• Subjects: Data; Data mining; Dimensions; Efficiency; GeoSOT; Location based services; Maintenance costs; Pattern analysis; Queries; Query processing; Spatial data; spatiotemporal index; spatiotemporal range query; Statistical methods; Storage; Traffic flow; trajectory data; Beijing China; China
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi8060284

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.