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Faster Algorithms for Tree Similarity Based on Compressed Enumeration of Bounded-Sized Ordered Subtrees

机译:基于有界有序子树压缩枚举的树相似度快速算法

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In this paper, we study efficient computation of tree similarity for ordered trees based on compressed subtree enumeration. The compressed subtree enumeration is a new paradigm of enumeration algorithms that enumerates all subtrees of an input tree T in the form of their compressed bit signatures. For the task of enumerating all compressed bit signatures of k-subtrees in an ordered tree T, we first present an enumeration algorithm in O(k)-delay, and then, present another enumeration algorithm in constant-delay using O(n) time preprocessing that directly outputs bit signatures. These algorithms are designed based on bit-parallel speed-up technique for signature maintenance. By experiments on real and artificial datasets, both algorithms showed approximately 22% to 36% speed-up over the algorithms without bit-parallel signature maintenance.
机译:在本文中,我们研究了基于压缩子树枚举的有序树的树相似度的有效计算。压缩子树枚举是枚举算法的新范例,该枚举算法以其压缩位签名的形式枚举输入树T的所有子树。对于枚举有序树T中k个子树的所有压缩位签名的任务,我们首先提出O(k)延迟的枚举算法,然后提出使用O(n)时间以恒定延迟的枚举算法直接输出位签名的预处理。这些算法是基于位并行加速技术设计的,用于签名维护。通过在真实数据集和人工数据集上的实验,这两种算法均比没有位并行签名维护的算法提高了约22%至36%的速度。

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