Reverse nearest neighbor search with a non-spatial aspect

With the recent surge in the use of the location-based service (LBS), the importance of spatial database queries has increased. The reverse nearest neighbor (RNN) search is one of the most popular spatial database queries. In most previous studies, the spatial distance is used for measuring the distance between objects. However, as the demands of users of the LBSs are becoming more complex, considering only the spatial factor as a distance measure is not sufficient. For example, through a hotel finding service, users want to choose a hotel considering not only the spatial distance, but also the non-spatial aspect of the hotel such as the quality which can be represented by the number of stars. Therefore, services that consider both spatial and non-spatial factors in measuring the distance are more useful for users. In such a case, techniques proposed in the previous studies cannot be used since the distance measure is different. In this paper, we propose an efficient method for the RNN search in which a distance measure involves both the spatial distance and the non-spatial aspect of an object. We conduct extensive experiments on a large dataset to evaluate the efficiency of the proposed method. The experimental results show that the proposed method is significantly efficient and scalable. •We firstly address the reverse nearest neighbor search with a non spatial aspect.•We propose an effective method to filter out items before performing the RNN search.•An efficient algorithm for our problem, based on a 2-layered structure, is proposed.•Experiments using synthetic/real datasets show our method is efficient and scalable.