Group nearest neighbor queries in the presence of obstacles

In this paper, we introduce obstructed group nearest neighbor (OGNN) queries, that enable a group to meet at a point of interest (e.g., a restaurant) with the minimum aggregate travel distance in an obstructed space. In recent years, researchers have focused on developing algorithms for processing GNN queries in the Euclidean space and road networks, which ignore the impact of obstacles such as buildings and lakes in computing distances. We propose the first comprehensive approach to process an OGNN query. We present an efficient algorithm to compute aggregate obstructed distances, which is an essential component for processing OGNN queries. We exploit geometric properties to develop pruning techniques that reduce the search space and incur less processing overhead. We validate the efficacy and efficiency of our solution through extensive experiments using both real and synthetic datasets.