Indexing large moving objects from past to future with PCFI

In moving object database, the moving objects' current position must be kept in memory, also to the trajectory, in some case, as same as the future. But the current existing indexes such as SEB-tree, SETI-tree, 2+3R-tree, 2-3RT-tree and etc. can only provide the capability for past and current query, and the TPR-Tree, TPR*-Tree and etc.can only provide the capability for current and future query. None of them can provide a strategy for indexing the past, current and also the future information of moving objects.In this paper, we propose the past-current-future Index (PCFI-Index) to index the past,current & future information of the moving objects. It is the combination of SETI-tree and TPR*-tree, the SETI liking index is used for indexing the historical trajectory segments except the front line structure, and the moving objects' current positions, velocities are indexed via the in-memory frontline structure which mainly implemented with TPR*-tree.Considering the large update operations on TPR-tree of large population, a hash table considering cache sensitivity is also introduced. It works with the frontline part, leading a bottom-up update of the tree. The performance analysis proves that the PCFI-index can handle most of the query efficiently and provides a uniform solution for the trajectory query, time-slice query, internal query and moving query.     

Indexing large moving objects from past to future with PCFI

In moving object database,the moving objects’current position must be kept in memory,also to the trajectory,in some case,as same as the future. But the current existing indexes such as SEB-tree,SETI-tree,2+3R-tree,2-3RT-tree and etc. can only provide the capability for past and current query,and the TPR-Tree,TPR* -Tree and et. can only provide the capability for current and future query. None of them can provide a strategy for indexing the past,current and also the future information of moving objects. In this paper,we propose the past-current-future Index (PCFI-Index) to index the past,current & future information of the moving objects. It is the combination of SETI-tree and TPR*-tree,the SETI liking index is used for indexing the historical trajectory segments except the front line structure,and the moving objects’ current positions,velocities are indexed via the in-memory frontline strucltre which mainly implemented with TPR*-tree. Considering the large update operations on TPR-tree of large population,a hash table considering cache sensitivity is also introduced. It works with the frontline part,leading a bottom-up update of the tree. The performance analysis proves that the PCFI-iidex can handle most of the query efficiently and provides a uniform solution for the trajectory query,time-slice query,internal query and moving query.     

An index method for continuous moving objects

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 te 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 queryiig,because TPR-tree has a time horizon limit for predictive queryiig. Further more,a B-tre e will be use d 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:.     

An index method for continuous moving objects

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.     

An index method for continuous moving objects

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.     

1982—2014年华北及周边地区生长季NDVI变化及其与气候的关系

利用690个气象观测站数据和1982―2014年GIMMS NDVI 3g数据,运用趋势分析、小波偏互相关分析、偏相关分析和滞后分析方法,探究华北及周边地区33年来生长季(5―10月)NDVI的变化规律及其与气候的关系,得到如下结论.1)33年来,研究区植被生长季活动整体上显著增强,生长季NDVI由20世纪80年代的平...