Tsung-Han Ku

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Given a set $\mathcal{D}$ of patterns of total length n, the dictionary matching problem is to index $\mathcal{D}$ such that for any query text T, we can locate the occurrences of any pattern within T efficiently. This problem can be solved in optimal O(|T|+occ) time by the classical AC automaton (Aho and Corasick in Commun. ACM 18(6):333–340, 1975), where(More)
Given a set $\D$ of $d$ patterns of total length $n$, the dictionary matching problem is to index $\D$ such that for any query text $T$, we can locate the occurrences of any pattern within $T$ efficiently. This problem can be solved in optimal $O(|T|+occ)$ time by the classical AC automaton (Aho and Corasick, 1975) where $occ$ denotes the number of(More)
Let T = T1φ 1T2φ k2 · · ·φdTd+1 be a text of total length n, where characters of each Ti are chosen from an alphabet Σ of size σ, and φ denotes a wildcard symbol. The text indexing with wildcards problem is to index T such that when we are given a query pattern P , we can locate the occurrences of P in T efficiently. This problem has been applied in(More)
We present several results about position heaps, a relatively new alternative to suffix trees and suffix arrays. First, we show that, if we limit the maximum length of patterns to be sought, then we can also limit the height of the heap and reduce the worst-case cost of insertions and deletions. Second, we show how to build a position heap in linear time(More)
Hon et al. (2011) recently proposed a variant of suffix tree, called circular suffix tree, and showed that it can be compressed into succinct space and can be used to solve the circular dictionary matching problem efficiently. Although there are several efficient construction algorithms for the suffix tree in the literature, none of them can be applied(More)
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