基于时空连续性的运动对象最近邻居查询

时空数据库中的运动对象最近邻居查询是NN Queries中的新问题，基于TPR-TREE索引结构的TP NN Queries算法能较好地处理对象的时态特性，但会多次查询同一对象。本文利用运动对象的时空连续性对TP NN Queries算法进行改进，通过一次查询TPR-TREE索引获取所有候选NN对象与查询对象的距离变化曲线，进而得到NN对象集，减少了查询及时空运算的次数。本文最后给出了实验分析。

Moving Objects' NN Queries Based on Spatio-Temporal Continuity

In spatio-temporal databases, the moving objects' nearest neighbor query is a new problem of nearest neighbor queries(NN Queries). The current TP NN Queries method which is based on the index structure of TPR-TREE can deal with moving objects, but it has the shortcomings of repeatedly querying some moving objects. Based on the theory of moving objects' spatio-temporal continuity, we propose an improved method(TP NN Queries) that searches the TPR-TREE once to get the curve of distance changes between the query object and the candidate nearest neighbor set, and then we get the NN set. Our method cuts down the query and calculation times. Experimental results show that our method makes a practical improvement.