• The KIPS Transactions:PartA
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• v.17A no.5
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• pp.221-228
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• 2010

Since string operations were applied to computational biology, security and search for Internet, various data structures and algorithms for computing efficient string operations have been studied. The all-pairs suffix-prefix matching is to find the longest suffix and prefix among given strings. The matching algorithm is importantly used for fast approximation algorithm to find the shortest superstring, as well as for bio-informatics and data compressions. In this paper, we propose an algorithm to find all-pairs suffix-prefix matching using the suffix array, which takes O($k{\cdot}m$)�� time complexity. The suffix array algorithm is proven to be better than the suffix tree algorithm by showing it takes less time and memory through experiments.

• Journal of Computing Science and Engineering
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• v.3 no.1
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• pp.1-4
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• 2009

In a given text T of size n, we need to search for the information that we are interested. In order to support fast searching, an index must be constructed by preprocessing the text. Suffix array is a kind of index data structure. The compressed suffix array (CSA) is one of the compressed indices based on the regularity of the suffix array, and can be compressed to the $k^{th}$ order empirical entropy. In this paper we improve the lookup time complexity of the compressed suffix array by using the multi-ary wavelet tree at the cost of more space. In our implementation, the lookup time complexity of the compressed suffix array is O(${\log}_{\sigma}^{\varepsilon/(1-{\varepsilon})}\;n\;{\log}_r\;\sigma$), and the space of the compressed suffix array is ${\varepsilon}^{-1}\;nH_k(T)+O(n\;{\log}\;{\log}\;n/{\log}^{\varepsilon}_{\sigma}\;n)$ bits, where a is the size of alphabet, $H_k$ is the kth order empirical entropy r is the branching factor of the multi-ary wavelet tree such that $2{\leq}r{\leq}\sqrt{n}$ and $r{\leq}O({\log}^{1-{\varepsilon}}_{\sigma}\;n)$ and 0 < $\varepsilon$ < 1/2 is a constant.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.6
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• pp.268-278
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• 2005

We consider constructing the generalized suffix way of strings A and B when the suffix arrays of A and B are given, j.e., merging two suffix arrays of A and B. There are efficient algorithms to merge some special suffix arrays such as the odd array and the even array. However, for the general case that A and B are arbitrary strings, no efficient merging algorithms have been developed. Thus, one had to construct the generalized suffix arrays of A and B by constructing the suffix array of A$\#$B$\$$ from scratch, even though the suffix ways of A and B are given. In this paper, we Present efficient merging algorithms for the suffix arrays of two arbitrary strings A and B drawn from constant and integer alphabets. The experimental results show that merging two suffix ways of A and B are about 5 times faster than constructing the suffix way of A$\#$B$\$$ from scratch for constant alphabets. Our algorithms include searching all suffixes of string B in the suffix array of A. To do this, we use suffix links in suffix ways and we developed efficient algorithms for computing the suffix links. Efficient computation of suffix links is another contribution of this paper because it can be used to solve other problems occurred in bioinformatics that should search all suffixes of a given string in the suffix array of another string such as computing matching statistics, finding longest common substrings, and so on. The experimental results show that our methods for computing suffix links is about 3-4 times faster than the previous fastest methods.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.521-523
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• 2006

This paper proposes an algorithm and its data structure to support real-time full-text search for the streamed or broadcasted multimedia data containing real-time stenograph text. Since the traditional indexing method used at information retrieval area uses the linguistic information, there is a heavy cost. Therefore, we propose the algorithm and its data structure based on suffix array, which is a simple data structure and has low space complexity. Suffix array is useful frequently to search for huge text. However, subtitle text of multimedia data is to get longer by time. Therefore, suffix array must be reconstructed because subtitle text is continually changed. We propose the data structure called prefix array and search algorithm using it.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.5_6
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• pp.169-175
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• 2007

Suffix arrays, fundamental full-text index data structures, can be efficiently used where patterns are queried many times. Although many useful full-text index data structures have been proposed, their O(nlogn)-bit space consumption motivates researchers to develop more space-efficient ones. However, their space efficient versions such as the compressed suffix array and the FM-index have been developed; those can not reduce the practical working space because their constructions are based on the existing suffix array. Recently, two direct construction algorithms of compressed suffix arrays from the text without constructing the suffix array have been proposed. In this paper, we compare practical performance of these algorithms of compressed suffix arrays with that of various algorithms of suffix arrays by measuring the construction times, the peak memory usages during construction and the sizes of their final outputs.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.2
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• pp.68-72
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• 2009

The suffix array is a data structure storing all suffixes of a string in lexicographical order. It is widely used in string problems instead of the suffix tree, which uses a large amount of memory space. Many researches have shown that not only the suffix array can be built in O(n), but also it can be constructed with a small time and space usage for real-world inputs. In this paper, we analyze a practical suffix sorting algorithm due to Maniscalco and Puglisi [1], and we propose an efficient algorithm which improves Maniscalco-Puglisi's running time.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1760-1766
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• 2010

The linearized suffix tree (LST) is an array data structure supporting traversals on suffix trees. We apply this LST to two dimensional (2D) suffix trees and obtain a space-efficient substitution of 2D suffix trees. Given an $n{\times}n$ text matrix and an $m{\times}m$ pattern matrix over an alphabet ${\Sigma}$, our 2D-LST provides pattern matching in $O(m^2log{\mid}{\Sigma}{\mid})$ time and $O(n^2)$ space.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.5
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• pp.260-267
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• 2005

To search efficiently a text T of length n for a pattern P over an alphabet 5, suffix trees and suffix arrays are widely used. In case of a large text, suffix arrays are preferred to suffix trees because suffix ways take less space than suffix trees. Recently, O(${\mid}P{\mid}{\codt}{\mid}{\Sigma}{\mid}$-time and O(${\mid}P{\mid}P{\cdot}log{\mid}{\Sigma}{\mid}$)-time search algorithms in suffix ways were developed. In this paper we present time and space efficient search algorithms in suffix arrays. One algorithm runs in O(${\mid}P{\mid}$) time using O($n{\cdot}{\mid}{\Sigma}{\mid}$)-bits space, and the other runs in O($n{\cdot}{\mid}{\Sigma}{\mid}$ time using O($nlog{\mid}{\Sigma}{\mid}+{\mid}{\Sigma}{\mid}{\cdot}$nlog log n/logn)-bits space, which is more space efficient and still fast. Experiments show that our algorithms are efficient in both time and space when compared to previous algorithms.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.319-326
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• 2007

To perform fast searching in massive data such as DNA strings, the most efficient method is to construct full-text index data structures of given strings. The widely used full-text index structures are suffix trees and suffix arrays. Since the suffix may uses less space than the suffix tree, the suffix array is proper for DNA strings. Previously developed construction algorithms of suffix arrays are not suitable for DNA strings since those are designed for integer alphabets. We propose a fast algorithm to construct suffix arrays on DNA strings whose alphabet sizes are fixed by 4. We reduce the construction time by improving encoding and merging steps on Kim et al.[1]'s algorithm. Experimental results show that our algorithm constructs suffix arrays on DNA strings 1.3-1.6 times faster than Kim et al.'s algorithm, and also for other algorithms in most cases.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.598-607
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• 2005

We propose a new external-memory index data structure, the Suffix B-tree. The Suffix B-tree is a B-tree in which the key is a string like the String B-tree. While the node in the String B-tree is implemented with a Patricia trio, the node in the Suffix B-tree is implemented with an array. So the Suffix B-tree is simpler and easier to be Implemented than the String B-tree. Nevertheless, the branching algorithm of the Suffix B-tree is as efficient as that of the String B-tree. Consequently, the Suffix B-tree takes the same worst-case disk accesses as the String B-tree to solve the string matching problem, which is fundamental and important in the area of string algorithms.