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    刘俊岭, 于 戈, 孙焕良. 空间数据库中主题相关区域查询[J]. 计算机研究与发展, 2012, 49(10): 2171-2180.
    引用本文: 刘俊岭, 于 戈, 孙焕良. 空间数据库中主题相关区域查询[J]. 计算机研究与发展, 2012, 49(10): 2171-2180.
    Liu Junling, Yu Ge, Sun Huanliang. Topic-relevant Region Queries in Spatial Database[J]. Journal of Computer Research and Development, 2012, 49(10): 2171-2180.
    Citation: Liu Junling, Yu Ge, Sun Huanliang. Topic-relevant Region Queries in Spatial Database[J]. Journal of Computer Research and Development, 2012, 49(10): 2171-2180.

    空间数据库中主题相关区域查询

    Topic-relevant Region Queries in Spatial Database

    • 摘要: 空间查询处理已经广泛地应用于基于位置的服务、设施选址等领域.提出一种新的空间查询:主题相关区域查询(topic-relevant region queries, T2R),该查询可以用于位置选址等空间决策分析.给定一个由空间特征对象集合R定义的主题T、查询窗口q,T2R查询返回不交叠的k个与主题最相关的区域,区域与主题的相关程度由区域内特征对象的数量结合其重要性进行计算.为了有效处理T2R查询,提出BSL,FR和SHR 3种算法,其中SHR算法将高相关程度区域先聚类、再收缩以获得更优的剪枝效果.所提出的算法解决了给定查询窗口下对数据空间任意位置按主题相关程度进行排序的问题.利用真实与人工数据集进行了充分实验,评估了所提出算法在不同参数设置下的查询效率,通过针对实际主题的查询验证了T2R查询的有效性.

       

      Abstract: Spatial query processing is widely applied for the location-based service and the facility selection. This paper proposes and solves a novel type of spatial queries named Topic-Relevant Region (T2R) queries, which can be used to spatial decision analysis and location selection. Given a topic T defined by a feature set R, a query window q, a T2R query retrieves k non-overlapping regions that have the highest relevance values computed by the number of feature objects and their importance. As an example, the finance topic is defined by stock exchanges, insurance companies and banks. On the topic, T2R query finds finance centers intensively distributed feature objects. We propose three efficient algorithms to process T2R, which are Baseline (BSL), Filtering-Refinement (FR) and Shrink (SHR) algorithm. SHR can obtain the best pruning performance by clustering regions with high relevance values and shrinking them compared with the other two algorithms. The proposed algorithms solve problem of sorting the regions according to their relevance values with the topic at an arbitrary location. We evaluate the efficiency of the proposed algorithms with extensive experiments on real and synthetic datasets under a wide range of parameter settings and verify the effectiveness of T2R query by the real topic query.

       

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