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Approximate Membership Localization (AML) for Web-Based Join

机译:基于Web的连接的近似会员定位(AML)

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In this paper, we propose a search-based approach to join two tables in the absence of clean join attributes. Non-structured documents from the web are used to express the correlations between a given query and a reference list. To implement this approach, a major challenge we meet is how to efficiently determine the number of times and the locations of each clean reference from the reference list that is approximately mentioned in the retrieved documents. We formalize the Approximate Membership Localization (AML) problem and propose an efficient partial pruning algorithm to solve it. A study using real-word data sets demonstrates the effectiveness of our search-based approach, and the efficiency of our AML algorithm.
机译:在本文中,我们提出了一种基于搜索的方法,在没有干净的连接属性的情况下加入两个表。来自Web的非结构化文档用于表示给定查询和引用列表之间的相关性。为了实现这种方法,我们遇到的主要挑战是如何有效地确定来自检索到的文档中大致提到的参考列表的次数和每个清洁参考的次数。我们正规化近似会员定位(AML)问题,并提出了一个有效的部分修剪算法来解决它。使用实际数据集的研究展示了我们的搜索方法的有效性,以及我们的AML算法的效率。

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