An Efficient Approach for Indoor Facility Location Selection

The advancement of indoor location-aware technologies enables a wide range of location based services in indoor spaces. In this paper, we formulate a novel Indoor Facility Location Selection (IFLS) query that finds the optimal location for placing a new facility (e.g., a coffee station) in an indoor venue (e.g., a university building) such that the maximum distance of all clients (e.g., staffs/students) to their nearest facility is minimized. To the best of our knowledge we are the first to address this problem in an indoor setting. We first adapt the state-of-the-art solution in road networks for indoor settings, which exposes the limitations of existing approaches to solve our problem in an indoor space. Therefore, we propose an efficient approach which prunes the search space in terms of the number of clients considered, and the total number of facilities retrieved from the database, thus reducing the total number of indoor distance calculations required. The key idea of our approach is to use a single pass on a state-of-the-art index for an indoor space, and reuse the nearest neighbor computation of clients to prune irrelevant facilities and clients. We evaluate the performance of both approaches on four indoor datasets. Our approach achieves a speedup from 2 . 84 × to 71 . 29 × for synthetic data and 97 . 74 × for real data over the baseline.