基于贪心策略的最近邻Top-k偏好查询方法

Greedy Strategy Based Nearest Neighbor Top-k Preference Query Method

传统Top-k空间关键字查询忽略了兴趣对象周围的基础设施属性对于用户偏好的影响,针对该问题,研究了基于影响区域约束关系的Top-k空间关键字偏好查询问题,设计了一种基于贪心策略的最近邻算法GS-NNA(Greedy Strategy based Nearest Neighbor Algorithm)。该算法采用R^*-tree和倒排文件两种索引结构,结合贪心思想和最近邻算法,每次选择分值最高的兴趣对象作为候选结果集,并利用阈值判定条件对R^*-tree进行剪枝。实验结果表明,GS-NNA算法与现有相关算法相比,有效提高了查询效率。

Traditional Top-k spatial keyword query ignores the influence of infrastructure attributes around the interest object on user preference.Aiming at this problem,the Top-k spatial keyword preference query problem based on the constraint relation of influence region is studied,and a Greedy Strategy based Nearest Neighbor Algorithm(GS-NNA)is designed.This algorithm adopts R^*-tree and inverted file index structure,and combines the greedy idea and the nearest neighbor algorithm.Then GS-NNA algorithm selects the interest object with the highest score as the candidate result set each time.The experimental results show that GS-NNA algorithm can effectively improve the query efficiency compared with the existing related algorithms.