针对面向高度动态移动对象集的多用户连续K近邻查询,提出了基于查询索引的多用户连续K近邻查询处理(Query Index based Multiple Continuous K-Nearest Neighbor Queries,QI-MCKNN)算法,阐述了查询索引的概念和构建方法,分析了格网大小...针对面向高度动态移动对象集的多用户连续K近邻查询,提出了基于查询索引的多用户连续K近邻查询处理(Query Index based Multiple Continuous K-Nearest Neighbor Queries,QI-MCKNN)算法,阐述了查询索引的概念和构建方法,分析了格网大小对查询性能的影响,给出了相应的查询处理算法。实验表明,算法在面对高度动态的移动对象集时,查询处理性能优于基于移动对象格网索引的SEA-CNN算法。展开更多
A novel technique called the bitmap lattice index(BLI) is proposed, which combines the advantages of a wireless broadcasting environment with a road network. Existing road networks are based on the on-demand method: a...A novel technique called the bitmap lattice index(BLI) is proposed, which combines the advantages of a wireless broadcasting environment with a road network. Existing road networks are based on the on-demand method: a server's workload increases as the query request increases when a server sends a client information. To solve this problem, we propose the BLI. The BLI denotes an object and a node as 0 and 1 in the Hilbert curve(HC) map. The BLI can identify the position of a node and an object through bit information; it can also reduce the broadcasting frequency of a server by reducing the size of the index, thereby decreasing the access latency and query processing times. Moreover, the BLI is highly effective for data filtering, as it can identify the positions of both an object and a node. In a road network, if filtering is done via the Euclidean distance, it may result in an error. To prevent this, we add another validation procedure. The experiment is conducted by applying the BLI to kNN query, and the technique is assessed by a performance evaluation experiment.展开更多
文摘针对面向高度动态移动对象集的多用户连续K近邻查询,提出了基于查询索引的多用户连续K近邻查询处理(Query Index based Multiple Continuous K-Nearest Neighbor Queries,QI-MCKNN)算法,阐述了查询索引的概念和构建方法,分析了格网大小对查询性能的影响,给出了相应的查询处理算法。实验表明,算法在面对高度动态的移动对象集时,查询处理性能优于基于移动对象格网索引的SEA-CNN算法。
基金supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF2013R1A1A1004593, 2013R1A1A1A05012348)
文摘A novel technique called the bitmap lattice index(BLI) is proposed, which combines the advantages of a wireless broadcasting environment with a road network. Existing road networks are based on the on-demand method: a server's workload increases as the query request increases when a server sends a client information. To solve this problem, we propose the BLI. The BLI denotes an object and a node as 0 and 1 in the Hilbert curve(HC) map. The BLI can identify the position of a node and an object through bit information; it can also reduce the broadcasting frequency of a server by reducing the size of the index, thereby decreasing the access latency and query processing times. Moreover, the BLI is highly effective for data filtering, as it can identify the positions of both an object and a node. In a road network, if filtering is done via the Euclidean distance, it may result in an error. To prevent this, we add another validation procedure. The experiment is conducted by applying the BLI to kNN query, and the technique is assessed by a performance evaluation experiment.
