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• W1971005684 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 (RSK k NN) query, which finds those objects that have the query as one of their k -nearest spatial-textual objects. The RSK k NN queries have numerous applications in online maps and GIS decision support systems. To answer RSK k NN 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 (C 2 IUR-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." @default.
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• W1971005684 date "2014-05-01" @default.
• W1971005684 modified "2023-10-07" @default.
• W1971005684 title "Efficient Algorithms and Cost Models for Reverse Spatial-Keyword <i>k</i> -Nearest Neighbor Search" @default.
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• W1971005684 doi "https://doi.org/10.1145/2576232" @default.
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