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A Metadata Search Approach with Branch and Bound Algorithm to Keyword Query in Relational Databases

机译:具有分支和绑定算法的元数据搜索方法,以关键词查询在关系数据库中

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Search engines on the Web have popularized the keyword-based search paradigm, while searching in databases users need to know a database schema and a query language. Keyword search techniques on the Web cannot directly be applied to databases because the data on the Internet and database are in different forms. So keyword search systems in relational databases have recently proposed. However existing systems limit type of keywords to database value terms, and generally assumed that answers are in instance level. Thus, this research aims to propose an effective approach for free-form keyword searching in databases which allows users to search either with database value terms, metadata terms or user terms. The metadata model accommodates these terms as well as underlying database semantics. Moreover, we present a branch and bound algorithm used for finding the optimal answer graphs. Our preliminary experiment results confirm precision of our approach.
机译:Web上的搜索引擎已经升级了基于关键字的搜索范例,同时在数据库中搜索用户需要了解数据库模式和查询语言。 Web上的关键字搜索技术不能直接应用于数据库,因为Internet和数据库上的数据具有不同的形式。因此,最近提出了关系数据库中的关键字搜索系统。然而,现有系统限制了数据库值术语的关键字类型,并且通常假设答案in实例级别。因此,本研究旨在提出有效的方法,用于在数据库中进行自由格式关键字搜索,这允许用户使用数据库值术语,元数据项或用户术语进行搜索。元数据模型适用于这些术语以及基础数据库语义。此外,我们介绍用于查找最佳答案图的分支和绑定算法。我们的初步实验结果确认了我们的方法精确。

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