There are current, historical and future information about continuously moving spatio temporal objects. And there are correspondingly spatio temporal indexes for current, past and future querying. Among the various ty...There are current, historical and future information about continuously moving spatio temporal objects. And there are correspondingly spatio temporal indexes for current, past and future querying. Among the various types of spatio temporal access methods, no one can support historical and future information querying. The Time Parameterized R tree(TPR tree) employs the idea of parametric bounding rectangles in the R tree. It can effectively support predictive querying to continuously moving objects. Unfortunately, TPR tree can not used to historical querying. This paper presents a partial persistence method in order to extend TPR tree for querying past information of moving objects. In this method, several TPR trees will be created for more effectively predictive querying, because TPR tree has a time horizon limit for predictive querying. Further more, a B tree will be used to index time dimension. Since the partial persistence method brings about huge storage space using, this paper also discusses some methods on how to reduce storage space. Finally, this paper presents an extensive experimental study for the proposed method and gives some interesting directions for future work.展开更多
基金This work is supported by the Major Project of National Natural Science Foundation (4 0 2 35 0 5 6 ) andthe Major Project of Natural Science Foundation of Beijing(4 0 110 0 2 )
文摘There are current, historical and future information about continuously moving spatio temporal objects. And there are correspondingly spatio temporal indexes for current, past and future querying. Among the various types of spatio temporal access methods, no one can support historical and future information querying. The Time Parameterized R tree(TPR tree) employs the idea of parametric bounding rectangles in the R tree. It can effectively support predictive querying to continuously moving objects. Unfortunately, TPR tree can not used to historical querying. This paper presents a partial persistence method in order to extend TPR tree for querying past information of moving objects. In this method, several TPR trees will be created for more effectively predictive querying, because TPR tree has a time horizon limit for predictive querying. Further more, a B tree will be used to index time dimension. Since the partial persistence method brings about huge storage space using, this paper also discusses some methods on how to reduce storage space. Finally, this paper presents an extensive experimental study for the proposed method and gives some interesting directions for future work.
