The Flexible Group Spatial Keyword Query

We present a new class of service for location based social networks, called the Flexible Group Spatial Keyword ‹ery, which enables a group of users to collectively €nd a point of interest (POI) that optimizes an aggregate cost function combining both spatial distances and keyword similarities. In addition, our query service allows users to consider the trade-o‚s between obtaining a suboptimal solution for the entire group and obtaining an optimimized solution but only for a subgroup. We propose algorithms to process three variants of the query: (i) the group nearest neighbor with keywords query, which €nds a POI that optimizes the aggregate cost function for the whole group of size n, (ii) the subgroup nearest neighbor with keywords query, which €nds the optimal subgroup and a POI that optimizes the aggregate cost function for a given subgroup size m (m ≤ n), and (iii) the multiple subgroup nearest neighbor with keywords query, which €nds optimal subgroups and corresponding POIs for each of the subgroup sizes in the range [m,n]. We design query processing algorithms based on branch-and-bound and best-€rst paradigms. Finally, we provide theoretical bounds and conduct extensive experimentswith two real datasets which verify the e‚ectiveness and eciency of the proposed algorithms.