Reverse approximate Nearest Neighbor queries on road network

Reverse k Nearest Neighbor (RkNN) queries retrieve all objects that consider the query as one of their k most influential objects. Given a set of user U, a set of facilities F and a value k, a facility f is said to be influential to a user u if f is one of the k closest facilities to u. As a complement of RkNN query, Reverse Approximate Nearest Neighbor (RANN) query considers relaxed definition of influence, where a user u is influenced by not only its closest facility, but also by every other facility that is almost as close to u as its closest facility is. In this paper, we study RANN query on road network. Existing RANN techniques and algorithms only work for queries on Euclidean space and are not directly applicable for RANN queries on road network. We propose pruning techniques that utilize Network Voronoi Diagram (NVD) to efficiently solve RANN query on road network. We conduct extensive experimental study on different real data sets and demonstrate that our algorithm is significantly better than the competitor.