CAJ | 학술논문

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(Top-k 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。
공간편호사순시당전공간사순연구중적일류열점문제，이현유적공간편호사순불능유효지지면향조용호적위치복무응용。위차，제출일류신형공간편호사순———면향조근린적 Top -k 공간편호사순(Top-k spatial preference query for group nearest neighbor)。해사순통과사조특정대상적λ자집조근린최종위용호반회평분치최고적전 k 개λ자집。위료고효집행저일사순，급출료량충사순산법：TSPQ -G 급 TSPQ -G*。기중TSPQ -G*재 TSPQ -G 적기출상，통과공간전지급고효적특정대상색인수편력책략대폭감소 I /O 대개，진이유효제고료해사순적집행효솔。실험채용다개수거집험증료소제산법재불동삼수설치하적유효성。
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 kλ-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.