Reverse spatial and textual k nearest neighbor search

Abstract

Geographic objects associated with descriptive texts are becoming prevalent. This gives prominence to spatial keyword queries that take into account both the locations and textual descriptions of content. Specifically, the relevance of an object to a query is measured by spatial-textual similarity that is based on both spatial proximity and textual similarity. In this paper, we define Reverse Spatial Textual k Nearest Neighbor (RSTkNN) query, i.e., finding objects that take the query object as one of their k most spatial-textual similar objects. Existing works on reverse kNN queries focus solely on spatial locations but ignore text relevance. To answer RSTkNN queries efficiently, we propose a hybrid index tree called IUR-tree (Intersection-Union R-Tree) that effectively combines location proximity with textual similarity. Based on the IUR-tree, we design a branch-andbound search algorithm. To further accelerate the query processing, we propose an enhanced variant of the IUR-tree called clustered IUR-tree and two corresponding optimization algorithms. Empirical studies show that the proposed algorithms offer scalability and are capable of excellent performance.