부산대학교 도서관

As an important decision support query, Group Nearest Neighbor (GNN) query has received considerable attention from Location Based Service (LBS) research community. Previous works paid much attention to the uncertain data objects (P). Nevertheless, very little work has done to the scenario when query objects (Q) are also uncertain. In this paper, The Range-based Probabilistic Group Nearest Neighbor (in short RP-GNN) query is introduced to draw a comprehensive discussion for this extended scenario. Two novel pruning methods are proposed to improve the performance of RP-GNN. The effectiveness, efficiency and scalability of proposed methods are validated through extensive experiments. The proposed methods achieve an average speed-up of 62.2% against existing probabilistic GNN algorithms and 1-2 orders of magnitude against linear scan.