Currently, many of the processing techniques for the location - based queries provide information of a single type of spatial objects, based on their spatial closeness to the query object. However, in real-life applications the user may be interested in obtaining information about different types of objects, in terms of their quality, cost, and neighboring relationship. We term the different types of objects with better quality and closer to each other the Neighboring skyline set (or NS set ). Three new types of location-based queries, the Distance - based neighboring skyline query ( Dist - NS query ), the Cost - based neighboring skyline query ( Cost - NS query ), and the Budget - based neighboring skyline query ( BGT - NS query ), are presented to determine the NS sets according to user’s specific requirement. A R-tree-based index, the $$R^{a,c}$$ R a , c - tree , is first designed to manage each type of objects with their locations, attributes, and costs. Then, a simultaneous traversal of the $$R^{a,c}$$ R a , c - trees built on different types of objects is employed with several pruning criteria to prune the non-qualifying object sets as early as possible, so as to improve the query performance. Extensive experiments using the synthetic dataset demonstrate the efficiency and the effectiveness of the proposed algorithms.

中文翻译：

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考虑距离、质量和成本，高效处理相邻的天际线查询

当前，基于位置的查询的许多处理技术基于它们与查询对象的空间接近度来提供单一类型空间对象的信息。然而，在实际应用中，用户可能对获取关于不同类型对象的信息感兴趣，包括质量、成本和相邻关系。我们将质量更好且彼此更接近的不同类型的物体称为相邻天际线集（或 NS 集）。三种新的基于位置的查询类型，基于距离的邻近天际线查询（Dist-NS 查询）、基于成本的邻近天际线查询（Cost-NS 查询）和基于预算的邻近天际线查询（BGT-NS 查询） )，根据用户的具体要求来确定 NS 集。基于 R 树的索引，$$R^{a, c}$$ R a ， c-tree ，首先设计用于管理每种类型的对象及其位置、属性和成本。然后，同时遍历建立在不同类型对象上的 $$R^{a,c}$$ R a , c - 树，并使用多个修剪标准来尽早修剪不合格的对象集，因此以提高查询性能。使用合成数据集的大量实验证明了所提出算法的效率和有效性。