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 ...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.展开更多
R Tree is a good structure for spatial searching. But in this indexing structure, either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibil...R Tree is a good structure for spatial searching. But in this indexing structure, either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibility that the object appears in different MBR which have the same parents node is different, if we make the subnode who has the most possibility be traveled first, the time cost will be decreased in most of the cases. In some case, the possibility of a point belong to a rectangle will shows direct proportion with the size of the rectangle. But this conclusion is based on an assumption that the objects are symmetrically distributing in the area and this assumption is not always coming into existence. Now we found a more direct parameter to scale the possibility and made a little change on the structure of R tree, to increase the possibility of founding the satisfying answer in the front sub trees. We names this structure probability based arranged R tree (PBAR tree).展开更多
基金This work is supported by University IT Ressearch Center Project in Korea.
文摘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.
基金This work is supported by University IT Research Center ProjectKorea.
文摘R Tree is a good structure for spatial searching. But in this indexing structure, either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibility that the object appears in different MBR which have the same parents node is different, if we make the subnode who has the most possibility be traveled first, the time cost will be decreased in most of the cases. In some case, the possibility of a point belong to a rectangle will shows direct proportion with the size of the rectangle. But this conclusion is based on an assumption that the objects are symmetrically distributing in the area and this assumption is not always coming into existence. Now we found a more direct parameter to scale the possibility and made a little change on the structure of R tree, to increase the possibility of founding the satisfying answer in the front sub trees. We names this structure probability based arranged R tree (PBAR tree).
