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中文摘要：
FNR-Tree利用2DR—Tree和1DR．Tree的结构，很好地结合了时间和空间的索引．但是随着索引数据量的增多，R-Tree本身的两个问题凸显出来（1）更新效率不高；（2）查询效率不高．本文在考虑了移动对象的时空同现的模式基础上，提出了一种对FNR-Tree优化的索引树FNRB-Tree，对于相同时间具有相同子轨迹的移动对象进行了按照路段的索引合并，从而达到了对FNR．Tree进行批量更新的效果．实验结果表明，FNRB-Tree在大数据量的情况下，（1）更新效率进行了提高；（2）对于邻近查询的响应时间更短．
英文摘要：
FNR-Tree （Fixed Network R-Tree） is a spatial-temporal indexing tree which combines 2D R-Tree and 1D R-Tree struc- ture. However, with the increasing amount of spatial-temporal data, two inherent R-Tree problems become even more prominent. { 1 } update costly; { 2 } query costly. In this paper, we improve FNR-Tree with spatial-temporal mining technique and cluster parallel moving objects in the same road segment together in FNR-Tree to construct FNRB-Tree { Fixed Network R-Tree with Buckets}. Experimental results show that in the case of a large mount spatial-temporal data, FNRB-Tree has two following advanta- ges： （ 1 } update efficiency improved; {2} better support for neighboring query.
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