On efficient mutual nearest neighbor query processing in spatial databases

Authors:
Yunjun Gao;Baihua Zheng;Gencai Chen;Qing Li
Affiliations:
School of Information Systems, Singapore Management University, 80 Stamford Road, Singapore 178902, Singapore and College of Computer Science, Zhejiang University, Hangzhou 310027, China;School of Information Systems, Singapore Management University, 80 Stamford Road, Singapore 178902, Singapore;College of Computer Science, Zhejiang University, Hangzhou 310027, China;Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Venue:
Data & Knowledge Engineering
Year:
2009

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Abstract

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 k"1 (=1) nearest neighbors (NNs) of q; meanwhile, have q as one of their k"2 (=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.