Nearest Neighbor （κNN） search is one of the most important operations in spatial and spatio-temporal databases. Although it has received considerable attention in the database literature, there is little prior work...Nearest Neighbor （κNN） search is one of the most important operations in spatial and spatio-temporal databases. Although it has received considerable attention in the database literature, there is little prior work on κNN retrieval for moving object trajectories. Motivated by this observation, this paper studies the problem of efficiently processing κNN （κ≥ 1） search on R-tree-like structures storing historical information about moving object trajectories. Two algorithms are developed based on best-first traversal paradigm, called BFPκNN and BFTκNN, which handle the κNN retrieval with respect to the static query point and the moving query trajectory, respectively. Both algorithms minimize the number of node access, that is, they perform a single access only to those qualifying nodes that may contain the final result. Aiming at saving main-memory consumption and reducing CPU cost further, several effective pruning heuristics are also presented. Extensive experiments with synthetic and real datasets confirm that the proposed algorithms in this paper outperform their competitors significantly in both efficiency and scalability.展开更多
Due to the famous dimensionality curse problem, search in a high-dimensional space is considered as a ＂hard＂ problem. In this paper, a novel composite distance transformation method, which is called CDT, is proposed...Due to the famous dimensionality curse problem, search in a high-dimensional space is considered as a ＂hard＂ problem. In this paper, a novel composite distance transformation method, which is called CDT, is proposed to support a fast κ-nearest-neighbor （κ-NN） search in high-dimensional spaces. In CDT, all （n） data points are first grouped into some clusters by a κ-Means clustering algorithm. Then a composite distance key of each data point is computed. Finally, these index keys of such n data points are inserted by a partition-based B^＋-tree. Thus, given a query point, its κ-NN search in high-dimensional spaces is transformed into the search in the single dimensional space with the aid of CDT index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of the proposed scheme. Our results show that this method outperforms the state-of-the-art high-dimensional search techniques, such as the X-Tree, VA-file, iDistance and NB-Tree.展开更多
文摘Nearest Neighbor （κNN） search is one of the most important operations in spatial and spatio-temporal databases. Although it has received considerable attention in the database literature, there is little prior work on κNN retrieval for moving object trajectories. Motivated by this observation, this paper studies the problem of efficiently processing κNN （κ≥ 1） search on R-tree-like structures storing historical information about moving object trajectories. Two algorithms are developed based on best-first traversal paradigm, called BFPκNN and BFTκNN, which handle the κNN retrieval with respect to the static query point and the moving query trajectory, respectively. Both algorithms minimize the number of node access, that is, they perform a single access only to those qualifying nodes that may contain the final result. Aiming at saving main-memory consumption and reducing CPU cost further, several effective pruning heuristics are also presented. Extensive experiments with synthetic and real datasets confirm that the proposed algorithms in this paper outperform their competitors significantly in both efficiency and scalability.
基金Partially supported by the National Natural Science Foundation of China （Grant No. 60533090）, National Science Fund for Distinguished Young Scholars （Grant No. 60525108）, the National Grand Fundamental Research 973 Program of China （Grant No. 2002CB312101）, Science and Technology Project of Zhejiang Province （Grant Nos. 2005C13032, 2005C11001-05） and China-America Academic Digital Library Project （see www.cadal.zju.edu.cn）.
文摘Due to the famous dimensionality curse problem, search in a high-dimensional space is considered as a ＂hard＂ problem. In this paper, a novel composite distance transformation method, which is called CDT, is proposed to support a fast κ-nearest-neighbor （κ-NN） search in high-dimensional spaces. In CDT, all （n） data points are first grouped into some clusters by a κ-Means clustering algorithm. Then a composite distance key of each data point is computed. Finally, these index keys of such n data points are inserted by a partition-based B^＋-tree. Thus, given a query point, its κ-NN search in high-dimensional spaces is transformed into the search in the single dimensional space with the aid of CDT index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of the proposed scheme. Our results show that this method outperforms the state-of-the-art high-dimensional search techniques, such as the X-Tree, VA-file, iDistance and NB-Tree.
