• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.996-998
• /
• 2009

접미사 트리는 주어진 하나의 문자열의 모든 접미사를 표현하는 트리로, 문자열 처리, 압축 등 다양한 분야에서 활용된다. 접미사 트리는 문자열 집합에 대한 자료구조로 확장될 수 있는데, 이를 일반화된 접미사 트리라 부른다. 본 논문에서는 일반화된 접미사 트리를 동반적이면서 온라인으로 생성하는 문제를 다룬다. 기존의 생성 알고리즘은 정방향의 문자열이 아닌 역방향의 문자열들에 대한 일반화된 접미사 트리를 생성하여, 부자연스럽다. 본 논문에서는 정방향 문자열들의 일반화된 접미사 트리를 동반적이면서 온라인으로 생성하는 알고리즘을 제시한다.

• Journal of KIISE
• /
• v.43 no.10
• /
• pp.1073-1078
• /
• 2016

(${\delta}$, ${\gamma}$)-matching for strings over integer alphabets can be applied to such fields as musical melody and share prices on stock markets. In this paper, we define ${\delta}$-approximate periods and ${\gamma}$-approximate periods of strings over integer alphabets. We also present two $O(n^2)$-time algorithms, each of which finds minimum ${\delta}$-approximate periods and minimum ${\gamma}$-approximate periods, respectively. Then, we provide the experimental results of execution times of both algorithms.

• Journal of KIISE:Computer Systems and Theory
• /
• v.34 no.8
• /
• pp.319-326
• /
• 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
• /
• v.32 no.9
• /
• pp.475-482
• /
• 2005

String data containing wildcard characters may represent certain patterns in texts. A subsumption relation between two patterns can be defined by a subset relation between sets of strings that match those patterns. Thus, the subsumption relation check is important to determine whether each pattern represents a set of strings without any overlap with another pattern. In this paper, we propose an effective algorithm that can determine subsumption relation between strings with wildcard characters. First, we consider a simple extension of the suffix tree algorithm so that it nay include wildcard characters and then we propose another method that checks the subsumption relation by dividing a suffix tree structure at each location of string data.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.1
• /
• pp.67-71
• /
• 2009

Recently, the string non-inclusion related problems have been studied vigorously. Given a set of strings F over a constant size alphabet, consider a string x such that x does not include any string in F as a substring. We call x a Common Non-SuperString(CNSS for short) of F. Among the CNSS's of F, the longest one with finite length is called the Longest Common Non-SuperString(LCNSS for short) of F. In this paper, we first propose a new graph model using prefixes of F. Next, we suggest an O(N)-time algorithm for finding the LCNSS of F, where N is the sum of the lengths of all the strings in F.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.6
• /
• pp.434-442
• /
• 2003

Software internationalization, the process of making software easier to localize for specific languages, has deep implications when applied to speech technology, where the goal of the task lies in the very essence of the particular language. A greatdeal of work and fine-tuning has gone into language processing software based on ASCII or a single language, say English, thus making a port to different languages difficult. The inherent identity of a language manifests itself in its lexicon, where its character set, phoneme set, pronunciation rules are revealed. We propose a decomposition of the lexicon building process, into four discrete and sequential steps. For preprocessing to build a lexical model, we translate from specific language code to unicode. (step 1) Transliterating code points from Unicode. (step 2) Phonetically standardizing rules. (step 3) Implementing grapheme to phoneme rules. (step 4) Implementing phonological processes.

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

• Journal of KIISE
• /
• v.42 no.3
• /
• pp.289-294
• /
• 2015

The consensus problem is finding a representative string, called a consensus, of a given set S of k strings. Circular strings are different from linear strings in that the last symbol precedes the first symbol. Given a set S of circular strings of length n over an alphabet ${\Sigma}$, we first present an $O({\mid}{\Sigma}{\mid}nlogn)$ time parallel algorithm for finding a consensus of S minimizing both radius and distance sum when k=3 using O(n) threads. Then we present an $O({\mid}{\Sigma}{\mid}n^2logn)$ time parallel algorithm for finding a consensus of S minimizing distance sum when k=4 using O(n) threads. Finally, we compare execution times of our algorithms implemented using CUDA with corresponding sequential algorithms.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10a
• /
• pp.736-738
• /
• 2004

써픽스 배열은 정렬된 모든 써픽스들의 인덱스를 저장한 자료구조이며, 긴 문자열에서 임의의 패턴을 효율적으로 검색을 할 수 있는 자료구조이다. 비슷한 자료구조인 써픽스 트리에 비해 적은 공간을 사용하기 때문에 대용량의 텍스트에 대한 처리에 더 적합하다. 본 논문에서는 써픽스 배열을 빠르게 구축하는 방법을 제안하고, 써픽스 배열 구축 알고리즘들 중에서 빠르다고 알려진 Larsson and Sadakane 알고리즘, 대표적인 선형 시간 알고리즘인 Karkkainen and Sanders 알고리즘 및 최근에 발표된 고정길이 문자집합에 효율적인 Kim et al. 알고리즘과 성능을 비교한다. 실험 결과 본 논문에서 제안한 알고리즘이 전반적으로 빠르게 써픽스 배열을 구축하였다.

• Journal of KIISE
• /
• v.43 no.11
• /
• pp.1275-1280
• /
• 2016

Existing techniques for string similarity search first generate a set of candidate strings and then verify the candidates. The efficiency of string similarity search is highly dependent on candidate generation methods. State of the art techniques select fixed length q-grams from a query string and generate candidates using inverted lists of the selected q-grams. In this paper, we propose a technique to generate candidates using variable length grams of a query string and develop a dynamic programming algorithm that selects an optimal combination of variable length grams from a query string. Experimental results show that the proposed technique improves the performance of string similarity search compared with the existing techniques.