基于线性四分树的空间关键词最近邻查询方法研究  

SPATIAL KEYWORD NEAREST NEIGHBOR QUERY METHOD BASED ON LINEAR QUADTREE

作　　者：于启迪 吴雷 马昂[1] Yu Qidi;Wu Lei;Ma Ang(School of Economics and Management,Shijiazhuang Tiedao University,Shijiazhuang 050043,Hebei,China;School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China)

机构地区：[1]石家庄铁道大学经济管理学院,河北石家庄050043 [2]燕山大学信息科学与工程学院,河北秦皇岛066004

出　　处：《计算机应用与软件》2018年第11期81-88,107,共9页Computer Applications and Software

基　　金：国家自然科学基金项目(61303017);河北省自然科学基金项目(F2018210109);河北省教育厅重点项目(ZD2018040);石家庄铁道大学第四届优秀青年科学基金项目(Z661250444);国家级大学生创新创业训练计划项目(201710107006)

摘　　要：随着移动互联网的快速发展和智能移动设备的广泛普及,空间文本对象的数量在不断增大,随之而来的是开展空间关键词查询技术的研究。Top-k空间关键词搜索是从空间数据库中探索有用信息的重要途径。近年来已经提出了各种各样的混合索引技术,主要是将R树和倒排表结合起来,从而同时执行空间修剪和文本修剪。然而,随着数据量的快速增长,在索引维护成本和查询处理时间两个方面对现有的方法提出了很大的挑战。针对这一问题,在改进的线性四分树的基础上,提出一种基于自适应虚拟四分树的空间关键词最近邻查询算法Avqt。在真实数据上进行实验验证,结果表明该算法的有效性。With the rapid development of the mobile Internet and the widespread popularization of smart mobile devices,the number of spatial text objects is increasing,and the consequent spatial keyword query technology is also in full swing.Top-k spatial keyword query is an important way to explore useful information from spatial databases.In recent years,a variety of hybrid indexing techniques has been proposed,mainly combining R-trees with inverted lists and performing space trimming and text clipping at the same time.However,with the rapid growth of data volume,the existing methods have presented great challenges in terms of index maintenance cost and query processing time.To solve this problem,on the basis of the improved linear quadtree,we presented a spatial keyword nearest neighbor query algorithm Avqt based on adaptive virtual quadtree.Experimental verification on real data shows that the proposed algorithm is effective.

关 键 词：自适应线性四分树 空间文本对象 最近邻查询 空间关键词查询 移动计算 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]