Top-k Most Incremental Location Selection with Capacity Constraint

Bichromatic reverse nearest neighbor (BRNN) based query uses the number of reverse nearest customers to model the influence of a facility location. The query has great potential for real life applications and receives considerable attentions from spatial database studies. In real world, facilities are inevitably constrained by designed capacities. When the needs of service increase, facilities in those booming areas may suffer from overloading. In this paper, we study a new kind of BRNN related query. It aims at finding most promising candidate locations to increase the overall service quality. To efficiently answer the query, we propose an O(n log n) algorithm using pruning techniques and spatial indices. To evaluate the efficiency of proposed algorithm, we conduct extensive experiments on both real and synthetic datasets. The results show our algorithm has superior performance over the basic solution.