Maximizing bichromatic reverse k nearest neighbor with multi-level tags queries in spatial-textual databases

With the popularity of mobile smart devices, location-based services have been more widely used. Bichromatic Reverse k Nearest Neighbor (BRkNN) queries have become a hotspot in spatial-textual databases domain. In this paper, we extend the concept of traditional BRkNN method to process the object with multi-level tags in some specific scenes, and we propose a new type of query, called Maximized Bichromatic Reverse k Nearest Neighbor with Multi-Level Tags queries (MaxBRkNN-MLT), to find the optimal position of object with multi-level tags in the spatial-textual database. Unlike traditional methods, the number of the results of the MaxBRkNN-MLT query is maximized, which can cross the great divide between space and text. The query method proposed in this paper has a wide range of application scenes. For example, in the advertising industry, advertisers expect to find an optimal position, so that the ads with a given tag can attract the most users. Finally, experiments show that the MLT method has better query precision and execution efficiency than the baseline approach.