Linear-size suffix tries

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Linear-size suffix tries

Suffix trees are highly regarded data structures for text indexing and string algorithms [MCreight 76, Weiner 73]. For any given string w of length n = |w|, a suffix tree for w takes O(n) nodes and links. It is often presented as a compacted version of a suffix trie for w, where the latter is the trie (or digital search tree) built on the suffixes of w. Here the compaction process replaces each...

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Uncommon Suffix Tries

Common assumptions on the source producing the words inserted in a suffix trie with n leaves lead to a lnn height and saturation level. We provide an example of a suffix trie whose height increases faster than a power of n and another one whose saturation level is negligible with respect to lnn. Both are built from VLMC (Variable Length Markov Chain) probabilistic sources and are easily extende...

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Average profiles, from tries to suffix-trees

We build upon previous work of Fayolle (2004) and Park and Szpankowski (2005) to study asymptotically the average internal profile of tries and of suffix-trees. The binary keys and the strings are built from a Bernoulli source (p, q). We consider the average number pk,P(ν) of internal nodes at depth k of a trie whose number of input keys follows a Poisson law of parameter ν. The Mellin transfor...

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Compact Suffix Trees Resemble Patricia Tries: Limiting Distribution of Depth

Wojciech Szpankowskrl: Dept. of Computer Science Purdue University W. Lafayette, IN 47907 U.S.A. Suffix trees are the most frequently used data structure in algorithms on words. Despite this, little is known about their behavior in a probabilistic framework. In this paper, we consider the depth of a compact suffix tree, also known as the PAT tree, under some simple probabilistic assumptions. In...

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Compact Suffix Trees Resemble PATRICIA Tries: Limiting Distribution of the Depth

Suffix trees are the most frequently used data structures in algorithms on words. In this paper, we consider the depth of a compact suffix tree, also known as the PAT tree, under some simple probabilistic assumptions. For a biased memoryless source, we prove that the limiting distribution for the depth in a PAT tree is the same as the limiting distribution for the depth in a PATRICIA trie, even...

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ژورنال

عنوان ژورنال: Theoretical Computer Science

سال: 2016

ISSN: 0304-3975

DOI: 10.1016/j.tcs.2016.04.002