Efficient Algorithms and Cost Models for Reverse Spatial-Keyword k-Nearest Neighbor Search

Abstract

Geographic objects associated with descriptive texts are becoming prevalent, justifying the need for spatial-keyword queries that consider both locations and textual descriptions of the objects. 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 article, we introduce the Reverse Spatial-Keyword k-Nearest Neighbor (RSKkNN) query, which finds those objects that have the query as one of their k-nearest spatial-textual objects. The RSKkNN queries have numerous applications in online maps and GIS decision support systems. To answer RSKkNN queries efficiently, we propose a hybrid index tree, called IUR-tree (Intersection-Union R-tree) that effectively combines location proximity with textual similarity. Subsequently, we design a branch-and-bound search algorithm based on the IUR-tree. To accelerate the query processing, we improve IUR-tree by leveraging the distribution of textual description, leading to some variants of the IUR-tree called Clustered IUR-tree (CIUR-tree) and combined clustered IUR-tree (C2IUR-tree), for each of which we develop optimized algorithms. We also provide a theoretical cost model to analyze the efficiency of our algorithms. Our empirical studies show that the proposed algorithms are efficient and scalable