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    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/75850
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/75850


    Title: Finding targets with the nearest favor neighbor and farthest disfavor neighbor by a skyline query
    Authors: Lin, Y.-W.;Wang, E.T.;Chiang, Chieh-Feng;Chen, Arbee L. P.
    陳良弼
    Contributors: 資科系
    Keywords: Algorithms;Experiments;Houses;Data points;Dominance rules;Farthest neighbors;Nearest neighbors;Skyline query;Skyline query processing;Spatial database;Synthetic data;Query processing
    Date: 2014-04
    Issue Date: 2015-06-16 15:57:19 (UTC+8)
    Abstract: Finding the nearest neighbors and finding the farthest neighbors are fundamental problems in spatial databases. Consider two sets of data points in a two-dimensional data space, which represent a set of favor locations F, such as libraries and schools, and a set of disfavor locations D, such as dumps and gambling houses. Given another set of data points C in this space as houses for rent, one who needs to rent a house may need a recommendation which takes into account the favor and disfavor locations. To solve this problem, a new two-dimensional data space is employed, in which dimension X describes the distance from a data point c in C to its nearest neighbor in D and dimension Y describes the distance from c to its farthest neighbor in F. Notice that the larger value is preferred in dimension X while the smaller value is preferred in dimension Y. Following the above dominance rule, the recommendation for the house renting can be achieved by a skyline query. A naïve method to processing this query is 1) to find the nearest neighbor from D and the farthest neighbor from F for each data point in C and then 2) to construct a new two-dimensional data space based on the results from 1) and to apply any of the existing skyline algorithms to get the answer. In this paper, based on the quad-tree index, we propose an efficient algorithm to answer this query by combining the above two steps. A series of experiments with synthetic data and real data are performed to evaluate this approach and the experiment results demonstrate the efficiency of the approach. Copyright 2014 ACM.
    Relation: Proceedings of the ACM Symposium on Applied Computing, 2014, Pages 821-826, 29th Annual ACM Symposium on Applied Computing, SAC 2014; Gyeongju; South Korea; 24 March 2014 到 28 March 2014; 代碼 106778
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1145/2554850.2554863
    DOI: 10.1145/2554850.2554863
    Appears in Collections:[資訊科學系] 會議論文

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