可伸缩的增量连续k近邻查询处理  

Scalable Processing of Incremental Continuous k-Nearest Neighbor Queries

作　　者：廖巍[1] 熊伟[1] 王钧[1] 景宁[1] 钟志农[1] 

机构地区：[1]国防科学技术大学电子科学与工程学院,湖南长沙410073

出　　处：《软件学报》2007年第2期268-278,共11页Journal of Software

基　　金：国家自然科学基金No.60472031~~

摘　　要：针对基于TPR树(time-parameterized R-tree)索引的大量并发CKNN(continuous k-nearest neighbor)查询处理,提出了一种可伸缩的增量连续k近邻查询处理(scalable processing of incremental continuous k-nearest neighbor queries,简称SI-CNN)框架,通过引入搜索区域进行预裁剪以减少查询更新所需要的TPR树节点访问代价,并引入了增量结果表以保存候选对象,批量地更新查询结果集,具有良好的可伸缩性.基于SI-CNN框架提出了一种增量更新的SI-CNN查询处理算法,能够基于上次查询结果增量的更新查询,支持查询集合中加入或删除查询和移动对象数据集的插入、删除等动态更新操作.实验结果与分析表明,基于SI-CNN框架的SI-CNN算法可以很好地支持大量并发的CKNN查询处理,具有良好的实用价值.To evaluate the large collection of concurrent CKNN （continuous k-nearest neighbor） queries continuously, a scalable processing of the incremental continuous k-nearest neighbor （SI-CNN） framework is proposed by introducing searching region to filter the visiting TPR-tree （time-parameterized R-tree） nodes. SI-CNN framework exploits the incremental results table to buffer the candidate objects, flushes the objects into query results in bulk, efficiently processes the large number of CKNN queries concurrently, and has a perfect scalability. An incremental SI-CNN query update algorithm is presented, which evaluates incrementally based on the former query answers and supports the insertion or deletion of both query collection and moving objects. Experimental results and analysis show that SI-CNN algorithm based on SI-CNN framework can support a large set of concurrent CKNN queries perfectly, and has a good practical application.

关 键 词：连续K近邻查询 TPR树 SI—CNN框架 SI—CNN算法 增量处理 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]