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. ©, 2015, Northeastern University. All right reserved.