Reverse spatial and textual k nearest neighbor search

Jiaheng Lu, Ying Lu, Gao Cong
2011 Proceedings of the 2011 international conference on Management of data - SIGMOD '11  
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 (RSTk NN) query, i.e., finding objects that take the query object as
more » ... 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 RSTk NN 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.
doi:10.1145/1989323.1989361 dblp:conf/sigmod/LuLC11 fatcat:ix6r2f5jejgd5afvy4y24cul74