【摘要】 单纯型连续近邻链查询在空间数据查询、空间数据挖掘和网络搜索等领域具有重要意义。针对障碍物环境下动态数据集中的单纯型连续近邻链查询问题,着重考虑新增点和删除点对初始单纯型连续近邻链的影响,基于判定圆域对初始单纯型连续近邻链进行二次计算和判断,提出在数据集动态增大和动态减小环境下的OB_DYNSCNNC_ADD和OB_DYNSCNNC_DET查询算法,以实现对数据集的有效筛选和过滤。理论研究和实验分析表明,2种算法均能实现障碍物环境下动态数据集中的单纯型连续近邻链查询,并具有较高的查询效率。更多还原

【Abstract】 The Simple Continues Near Neighbor Chain(SCNNC) query has important significance in the spatial data query,spatial data mining,Web search,etc. According to the problem that the existing query methods can not handle the SCNNC query in dynamic dataset with obstacles,the influence of the points insertion and deletion to the SCNNC are considered. The OB_DYNSCNNC_ADD algorithm and the OB_DYNSCNNC_DET algorithm are given for the dataset increasing dynamically and the dataset decreasing dynamically based on the judging circle and the filtering methods. The performance of the methods are analyzed and compared by experiment. The theatrical study and the experimental results show that the algorithms have great advantages for the SCNNC query in dynamic dataset with obstacles.更多还原