On efficient mutual nearest neighbor query processing in spatial databases

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• 作者：Yunjun Gao ; Baihua Zheng ; Gencai Chen ; Qing Li
• 关键词：Query processing ; Nearest neighbor ; Spatial databases ; Algorithm
• 刊名：Data and Knowledge Engineering
• 出版年：2009
• 出版时间：August 2009
• 年：2009
• 卷：68
• 期：8
• 页码：705-727
• 全文大小：2742 K

文摘

This paper studies a new form of nearest neighbor queries in spatial databases, namely, mutual nearest neighbor (MNN) search. Given a set D of objects and a query object q, an MNN query returns from D, the set of objects that are among the k₁ (1) nearest neighbors (NNs) of q; meanwhile, have q as one of their k₂ (1) NNs. Although MNN queries are useful in many applications involving decision making, data mining, and pattern recognition, it cannot be efficiently handled by existing spatial query processing approaches. In this paper, we present the first piece of work for tackling MNN queries efficiently. Our methods utilize a conventional data-partitioning index (e.g., R-tree, etc.) on the dataset, employ the state-of-the-art database techniques including best-first based k nearest neighbor (kNN) retrieval and reverse kNN search with TPL pruning, and make use of the advantages of batch processing and reusing technique. An extensive empirical study, based on experiments performed using both real and synthetic datasets, has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.