Continuous visible nearest neighbor queries

In this paper, we identify and solve a new type of spatial queries, called continuous visible nearest neighbor (CVNN) search. Given a data set P, an obstacle set O, and a query line segment Paq, a CVNN query returns a set of 〈p, R〉 tuples such that p ∈ P is the nearest neighbor (NN) to every point r along the interval R ⊆ Paq as well as p is visible to r. Note that p may be NULL, meaning that all points in P are invisible to all points in R, due to the obstruction of some obstacles in O. In this paper, we formulate the problem and propose efficient algorithms for CVNN query processing, assuming that both P and O are indexed by R-trees. In addition, we extend our techniques to several variations of the CVNN query. Extensive experiments verify the efficiency and effectiveness of our proposed algorithms using both real and synthetic datasets.