Abstract: As the mobile terminals are more and more widely used, large-scale spatial-temporal data are being created. Large-scale spatial-temporal data have made location based service popular. Traditional spatial-temporal indexes are not capable of storing large-scale and frequently updated data and at the same time providing concurrent high resolution range query. This paper explores latest research productions on solving concurrent updating and queries on large-scale spatial-temporal data, and analyzes the characters and weaknesses of mainstream algorithms. Then this paper presents a new algorithm called CUU on solving storing, updating and queries on large-scale spatial-temporal data. It also talks about the accuracy of range query problem and its solution. The experimental results on real telecommunication data show that CUU is capable of offering efficient concurrent queries and update services.

Key words: spatial-temporal data, telecommunication, range query, concurrent, index

摘要： 移动终端的普及催生了海量的时空数据。由于有了数据基础的支持，基于位置的服务应用也随之普及。传统的时空数据存储方案既难以适用于存储规模庞大、频繁更新的数据，又很难提供并发、高精度的区域查询服务。因此，参考大规模时空数据并发查询更新问题领域的最新研究成果，分析了该领域主流算法的特点及缺陷，设计了适用于海量高更新频率的时空数据（移动通信数据）的查询与更新算法CUU，讨论了区域查询精度问题及其解决方案。在真实移动通信数据上的实验结果表明，CUU算法可以高效处理并发的时空数据查询与更新。

关键词: 时空数据, 移动通信, 区域查询, 并发, 索引