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    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/75552
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/75552


    Title: Top-n query processing in spatial databases considering bi-chromatic reverse k-nearest neighbors
    Authors: Li, C.-L.;Wang, E.T.;Huang, G.-J.;Chen, Arbee L. P.
    陳良弼
    Contributors: 資科系
    Keywords: BRkNN queries;Refinement algorithms;Reverse k-nearest neighbors;RkNN queries;Spatial database;State-of-the-art algorithms;Top-n queries;Triangle inequality;Algorithms;Computational geometry;Graphic methods;Membership functions;Query languages;Text processing;Query processing
    Date: 2014-06
    Issue Date: 2015-06-03 12:33:39 (UTC+8)
    Abstract: A reverse k-nearest neighbor (RkNN) query retrieves the data points which regard the query point as one of their respective k nearest neighbors. A bi-chromatic reverse k-nearest neighbor (BRkNN) query is a variant of the RkNN query, considering two types of data. Given two types of data G and C, a BRkNN query regarding a data point q in G retrieves the data points from C that regard q as one of their respective k-nearest neighbors among the data points in G. Many existing approaches answer either the RkNN query or the BRkNN query. Different from these approaches, in this paper, we make the first attempt to propose a top-n query based on the concept of BRkNN queries, which ranks the data points in G and retrieves the top-n points according to the cardinalities of the corresponding BRkNN answer sets. For efficiently answering this top-n query, we construct the Voronoi Diagram of G to index the data points in G and C. From the information associated with the Voronoi Diagram of G, the upper bound of the cardinality of the BRkNN answer sets for each data point in G can be quickly computed. Moreover, based on an existing approach to answering the RkNN query and the characteristics of the Voronoi Diagram of G, we propose a method to find the candidate region regarding a BRkNN query, which tightens the corresponding search space. Finally, based on the triangle inequality, we propose an efficient refinement algorithm for finding the exact BRkNN answers from the candidate regions. To evaluate our approach on answering the top-n query, it is compared with an approach which applies a state-of-the-art algorithm for answering the BRkNN query to each data point in G. The experiment results reveal that our approach has a much better performance. © 2014 Elsevier Ltd.
    Relation: Information Systems, 42, 123-138
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.is.2014.01.001
    DOI: 10.1016/j.is.2014.01.001
    Appears in Collections:[資訊科學系] 期刊論文

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