面向组近邻的Top-k空间偏好查询

空间偏好查询是当前空间查询研究中的一类热点问题,而现有的空间偏好查询不能有效支持面向组用户的位置服务应用.为此,提出一类新型空间偏好查询——面向组近邻的Top-k空间偏好查询(Topk spatial preference query for group nearest neighbor).该查询通过查找特征对象的λ子集组近邻最终为用户返回评分值最高的前k个λ子集.为了高效执行这一查询,给出了两种查询算法:TSPQ-G及TSPQ-G*.其中TSPQ-G*在TSPQ-G的基础上,通过空间剪枝及高效的特征对象索引树遍历策略大幅减少I/O代价,进而有效提高了该查询的执行效率.实验采用多个数据集验证了所提算法在不同参数设置下的有效性.

Top-k Spatial Preference Query for Group Nearest Neighbor

Spatial preference query is a popular focus of the current research on spatial queries. However， the present spatial preference queries cannot be used in the location-based services for group users. To solve this problem， a novel type of spatial preference query， namely， Top-k spatial preference query for group nearest neighbor (TSPG) was proposed， which retrieves the k λ-subsets with the highest score through finding λ-subsets group nearest neighbors of the feature objects. Two algorithms， namely， TSPQ-G and TSPQ-G* were designed for efficient query processing. Based on the TSPQ-G， the TSPQ-G* was developed by performing spatial pruning strategies and efficient traversal strategies of feature objects index， which effectively reduces I/O cost and improves query efficiency. Experimental results on several datasets demonstrated the effectiveness of the proposed algorithms for different setups.