基于MongoDB的轨迹大数据时空索引构建方法

近年来，随着计算机技术与无线传感器网络的发展，轨迹大数据越来越得到人们的关注.针对海量轨迹数据在存储与查询中出现的效率问题，文章基于文档型非关系型数据库MongoDB提出了一套基于四叉树的道路网时空索引，实现海量轨迹数据的高效查询.通过对太原市1915辆出租车的50万条轨迹数据进行时空查询，在不同数据量与不同并发数下测试道路网时空索引与MongoDB复合时空索引的效率表现.实验结果显示道路网时空索引在数据量大于10万时有较好表现，并能够适应不同并发数下的时空查询，验证了道路网时空索引构建方法的可行性和高效性.

Spatio-Temporal Indexing Method of Big Trajectory Data Based on MongoDB

In recent years, with the vigorous development of computer science and wireless sensor network, how to address big trajectory data is becoming a concerned issue increasingly. Because of massive trajectory data, there is an increasing focus on storage and search of big trajectory data. In view of this, based on the document type non relational database MongoDB, we propose a spatio-temporal index of road network which is based on quad-tree. For the 1915 taxis in Taiyuan, the 500,000 pieces of trajectory data are searched. With different data and different number of concurrency, we compare the efficiency of spatio-temporal index with that of MongoDB composite spatio-temporal index. Experimental results show that our method performs well when data volume is larger than 100000. It can adapt to spatio-temporal queries with different number of concurrency, proving that the method is feasible and efficient.