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    政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 >  Item 140.119/111935
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/111935

    Title: Determining top-k candidates by reverse constrained skyline queries
    Authors: 陳良弼
    Jheng, Ruei Sian
    Wang, En Tzu
    Chen, Arbee L. P.
    Contributors: 資訊管理系
    Keywords: Decision trees;Information management;Query processing;Object o;Potential customers;Pruning strategy;Quad trees;Range query;Skyline query;Straight-forward method;Top-k query;Indexing (of information)
    Date: 2015
    Issue Date: 2017-08-14 15:34:13 (UTC+8)
    Abstract: Given a set of criteria, an object o is defined to dominate another object o' if o is no worse than o' in each criterion and has better outcomes in at least a specific criterion. A skyline query returns each object that is not dominated by any other objects. Consider a scenario as follows. Given three types of datasets, including residents in a city, existing restaurants in the city, and candidate places for opening new restaurants in the city, where each restaurant and candidate place has its respective rank on a set of criteria, e.g., convenience of parking, we want to find the top-k candidate places that have the most potential customers. The potential customers of a candidate place is defined as the number of residents whose distance to this candidate is no larger than a given distance r and also regard this candidate as their skyline restaurants. In this paper, we propose an efficient method based on the quad-tree index and use four pruning strategies to solve this problem. A series of experiments are performed to compare the proposed method with a straightforward method using the R-tree index. The experiment results demonstrate that the proposed method is very efficient, and the pruning strategies very powerful.
    Relation: DATA 2015 - 4th International Conference on Data Management Technologies and Applications, Proceedings, (), 101-110
    Data Type: conference
    Appears in Collections:[資訊管理學系] 會議論文

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