基于Google S2的大数据时空索引及区域查询算法

Spatial-temporal Index and Region Query Algorithm for Big Data Based on Google S2

随着时代的发展,轨迹数据的数据量越来越大。提出使用LevelDB作为存储轨迹数据的数据库,解决了数据快速入库、短时间捞出大量数据的问题;同时使用Google S2作为轨迹数据的时空索引,并提出了一种区域查询算法。经实验证明,使用该索引进行区域查询,无论是在查询数据的精准度上,还是查询时间上,均优于基于GeoHash作为轨迹数据时空索引。

With the development of the times,the amount of track data is increasing.In this paper,LevelDB is proposed as a database to store track data,which solves the problem of fast data storage and large amount of data retrieved in a short time.At the same time,Google S2 is used as the space-time index of track data,and a region query algorithm is proposed.Experiments have shown that using this index for regional queries is superior to using GeoHash as a spatial-temporal index for track data,both in accuracy and in query time.