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Supporting Location-Based Approximate-Keyword Queries

机译:支持基于位置的近似关键字查询

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摘要

Many Web sites support keyword search on their spatial data, such as business listings and photos. In these systems, inconsistencies and errors can exist in both queries and the data. To bridge the gap between queries and data, it is important to support approximate keyword search on spatial data. In this paper we study how to answer such queries efficiently. We focus on a natural index structure that augments a tree-based spatial index with capabilities for approximate keyword search. We systematically study how to efficiently combine these two types of indexes, and how to search the resulting index to find answers. We develop three algorithms for constructing the index, successively improving the time and space efficiency by exploiting the textual and spatial properties of the data. We experimentally demonstrate the efficiency of our techniques on real, large datasets.
机译:许多网站都支持在其空间数据(例如公司列表和照片)上进行关键字搜索。在这些系统中,查询和数据中都可能存在不一致和错误。为了弥合查询和数据之间的鸿沟,支持对空间数据进行近似关键字搜索非常重要。在本文中,我们研究了如何有效地回答此类查询。我们专注于自然索引结构,该结构通过近似关键字搜索的功能来扩充基于树的空间索引。我们系统地研究了如何有效地组合这两种类型的索引,以及如何搜索结果索引以找到答案。我们开发了三种用于构造索引的算法,通过利用数据的文本和空间属性来依次提高时间和空间效率。我们通过实验证明了我们的技术在真实,大型数据集上的效率。

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