• Journal of Korean Medical classics
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• v.34 no.1
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• pp.37-45
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• 2021

Objectives : The purpose of this paper was to examine quantitative methods for estimating and detecting local text reuse in the texts of East Asian Traditional Medicine. Methods : We introduce techniques that estimate the volume of local text reuse with n-gram and those that directly detect the reuse with the Smith-Waterman algorithm (SW algorithm). Based on this, the estimation and detection of local text reuse were carried out for 『Donguibogam』 and 『Huangdineijing·Suwen』. Results : Estimates with n-gram had more errors than methods with SW algorithms. SW algorithms detected suspected strings directly with local text reuse, resulting in more accurate results. Conclusions : Although n-gram does not accurately find local text reuse, its high speed makes it a preferable method for certain purposes, such as screening similar documents. On the other hand, SW algorithms have the advantage of being relatively good at finding similar phrases suspected as local text reuse even if the strings do not completely match. However, due to its excessive consumption of time and computing resources, its benefits are limited to cases where precise results are required.

• Journal of Advanced Navigation Technology
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• v.15 no.2
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• pp.287-297
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• 2011

As electronic mails are being widely used for facility and speedness of information communication, as the amount of spam mails which have malice and advertisement increase and cause lots of social and economic problem. A number of approaches have been proposed to alleviate the impact of spam. These approaches can be categorized into pre-acceptance and post-acceptance methods. Post-acceptance methods include bayesian filters, collaborative filtering and e-mail prioritization which are based on words or sentances. But, spammers are changing those characteristics and sending to avoid filtering system. In the case of Korean, the abnormal usages can be much more than other languages because syllable is composed of chosung, jungsung, and jongsung. Existing formal expressions and learning algorithms have the limits to meet with those changes promptly and efficiently. So, we present an methods for recognizing Korean abnormal language(Koral) to improve accuracy and efficiency of filtering system. The method is based on syllabic than word and Smith-waterman algorithm. Through the experiment on filter keyword and e-mail extracted from mail server, we confirmed that Koral is recognized exactly according to similarity level. The required time and space costs are within the permitted limit.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11c
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• pp.2337-2340
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• 2002

본 연구는 생물정보학(Bioinformatics)의 가장 기초적인 분야중 하나인, 새롭게 밝혀진 유전자 서열과 이미 밝혀진 유전자 서열 사이의 유사성(similarity)이나 상동성(homology)을 찾기 위한 방법에 대한 연구 중 지역 서열정렬로 사용하는 알고리즘인 Smith-Waterman 알고리즘이 갖고 있는 문제를 파악한다. 긴 서열에 대한 선호를 막고 대신 부분적인 지역 정렬을 다수 개 찾아 정렬시키는 알고리즘을 제안하기로 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05b
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• pp.1019-1022
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• 2003

두 서열을 비교하여 유사성(similarity)이나 상동성(homology)를 찾기 위한 서열 정렬 방법 중에서 지역 정렬에 많이 사용되는 Smith-Waterman 알고리즘의 제한점인 Mosaic effect와 Shadow effect를 극복하기 위한 효율적인 방법을 살펴보고, 하나의 최대 값이 아닌 다수개의 최대 값을 찾아 다수개를 정렬함으로써 서열내에 존재 할 수 있는 다수개의 지역 정렬을 찾고 Normalized sequence alignment 알고리즘을 이용하여 서열 정렬된 결과들의 우선 순위를 매겨본다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.976-978
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• 2004

Chaining 알고리즘은 주어진 match 정보로부터 좋은 match 조합을 찾아내는 일종의 alignment 알고리즘으로 유전체 서열을 비교하는데 다양하게 응용되고 있다. 특히 서열 전체를 비교하는 대신 부분 서열을 비교할 때 사용할 수 있는 local chaining 알고리즘이 제안되었는데 본 논문은 이 기본적인 알고리즘이 Smith-waterman 알고리즘과 유사하며 따라서 비슷한 단점을 가지고 있음을 지적한다. 그리고 이를 해결하기 위해 X-drop과 정규화 된 정수를 고려하는 두 가지 기법을 적용하고 실험을 통해 개선 효과를 보인다.

• Journal of KIISE
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• v.41 no.12
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• pp.1007-1012
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• 2014

Software birthmarks are used to detect software plagiarism. For binaries, however, only a few birthmarks have been developed. In this paper, we propose a static approach to generate API sequences along major paths, which are analyzed from control flow graphs of the binaries. Since our API sequences are extracted along the most plausible paths of the binary codes, they can represent actual API sequences produced from binary executions, but in a more concise form. Our similarity measures use the Smith-Waterman algorithm that is one of the popular sequence alignment algorithms for DNA sequence analysis. We evaluate our static path-based API sequence with multiple versions of five applications. Our experiment indicates that our proposed method provides a quite reliable similarity birthmark for binaries.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.413-424
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• 2011

A variety of different metrics has been introduced to measure the similarity of two given sequences. These widely used metrics are ranging from spell correctors and categorizers to new sequence mining applications. Different metrics consider different aspects of sequences, but the essence of any sequence is extracted from the ordering of its elements. In this paper, we propose a novel sequence similarity measure that is based on all ordered pairs of one sequence and where a Hasse diagram is built in the other sequence. In contrast with existing approaches, the idea behind the proposed sequence similarity metric is to extract all ordering features to capture sequence properties. We designed a clustering problem to evaluate our sequence similarity metric. Experimental results showed the superiority of our proposed sequence similarity metric in maximizing the purity of clustering compared to metrics such as d2, Smith-Waterman, Levenshtein, and Needleman-Wunsch. The limitation of those methods originates from some neglected sequence features, which are considered in our proposed sequence similarity metric.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.119-132
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• 2007

In molecular biology, approximate subsequence search is one of the most important operations. In this paper, we propose an accurate and efficient method for approximate subsequence search in large DNA databases. The proposed method basically adopts a binary trie as its primary structure and stores all the window subsequences extracted from a DNA sequence. For approximate subsequence search, it traverses the binary trie in a breadth-first fashion and retrieves all the matched subsequences from the traversed path within the trie by a dynamic programming technique. However, the proposed method stores only window subsequences of the pre-determined length, and thus suffers from large post-processing time in case of long query sequences. To overcome this problem, we divide a query sequence into shorter pieces, perform searching for those subsequences, and then merge their results. To verify the superiority of the proposed method, we conducted performance evaluation via a series of experiments. The results reveal that the proposed method, which requires smaller storage space, achieves 4 to 17 times improvement in performance over the suffix tree based method. Even when the length of a query sequence is large, our method is more than an order of magnitude faster than the suffix tree based method and the Smith-Waterman algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.1044-1049
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• 2006

Multiple sequence alignment is a method to compare two or more DNA or protein sequences. Most of multiple sequence alignment tools rely on pairwise alignment and Smith-Waterman algorithm to generate an alignment hierarchy. Therefore, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST and CDD (Conserved Domain Database)search were combined with a clustering tool. Our clustering and annotating tool consists of constructing suffix tree, overlapping common subsequences, clustering gene sequences and annotating gene clusters by BLAST and CDD search. The system was successfully evaluated with 36 gene sequences in the pentose phosphate pathway, clustering 10 clusters, finding out representative common subsequences, and finally identifying functional domains by searching CDD database.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.851-856
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• 2005

The DNA and protein data of diverse species have been daily discovered and deposited in the public archives according to each established format. Database systems in the public archives provide not only an easy-to-use, flexible interface to the public, but also in silico analysis tools of unidentified sequence data. Of such in silico analysis tools, multiple sequence alignment [1] methods relying on pairwise alignment and Smith-Waterman algorithm [2] enable us to identify unknown DNA, protein sequences or phylogenetic relation among several species. However, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST was combined with a clustering tool. Our clustering and annotating tool is summarized as the following steps: (1) construction of suffix tree; (2) masking of cross-matching pairs; (3) clustering of gene sequences and (4) annotating gene clusters by BLAST search. The system was successfully evaluated with 22 gene sequences in the pyrubate pathway of bacteria, clustering 7 clusters and finding out representative common subsequences of each cluster