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Efficient Answering of Why-Not Questions in Similar Graph Matching

机译:高效的相似图匹配问题回答

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Answering questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph and a set of missing () graphs, we aim to modify into a new query graph such that the missing graphs are included in the new answer set of . We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for . Then, we present a two-phase algorithm to find the new query . In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for . We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets.
机译:回答数据库中的问题有望在许多领域中具有广泛的应用前景,因此,数据库研究领域引起了近期的关注。本文解决了在图数据库的相似图匹配中回答这些所谓问题的问题。给定一组初始查询图的答案图和一组missing()图,我们旨在修改为一个新查询图,以便将丢失图包括在的新答案集中。我们提出了一种近似解决方案来解决上述问题,因为最优解决方案难于计算。在我们的方法中,我们首先计算有界搜索空间和要最小化的距离。然后,我们提出一种两阶段算法来查找新查询。在第一阶段中,我们生成一组候选边以添加到有界搜索空间内的初始查询中,或从中删除,在第二阶段中,我们选择在第一阶段中生成的候选边的子集以最小化距离为。我们还通过对两个真实的数据集进行广泛的实验来证明我们的方法的有效性和效率。

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