• Journal of KIISE:Computer Systems and Theory
• /
• v.32 no.5
• /
• pp.255-259
• /
• 2005

To search a pattern P in a text, such index data structures as suffix trees and suffix arrays are widely used in diverse applications of string processing and computational biology. It is well known that searching in suffix trees is faster than suffix ways in the aspect of time complexity, i.e., it takes O(${\mid}P{\mid}$) time to search P on a constant-size alphabet in a suffix tree while it takes O(${\mid}P{\mid}+logn$) time in a suffix way where n is the length of the text. In this paper we present a linear-tim8 search algorithm in suffix arrays for constant-size alphabets. For a gene.al alphabet $\Sigma$, it takes O(${\mid}P{\mid}log{\mid}{\Sigma}{\mid}$) time.

• KIPS Transactions on Software and Data Engineering
• /
• v.1 no.1
• /
• pp.31-42
• /
• 2012

In order to process the suffix derived nouns of Korean, most of Korean processing systems have been registering the suffix derived nouns in dictionary. However, this approach is limited because the suffix is very high productive. Therefore, it is necessary to analyze semantically the unregistered suffix derived nouns. In this paper, we propose a method to disambiguate homograph suffixes using Korean lexical semantic network(U-WIN) for the purpose of semantic analysis of the suffix derived nouns. 33,104 suffix derived nouns including the homograph suffixes in the morphological and semantic tagged Sejong Corpus were used for experiments. For the experiments first of all we semantically tagged the homograph suffixes and extracted root of the suffix derived nouns and mapped the root to nodes in the U-WIN. And we assigned the distance weight to the nodes in U-WIN that could combine with each homograph suffix and we used the distance weight for disambiguating the homograph suffixes. The experiments for 35 homograph suffixes occurred in the Sejong corpus among 49 homograph suffixes in a Korean dictionary result in 91.01% accuracy.

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

• Koreanishche Zeitschrift fur Deutsche Sprachwissenschaft
• /
• v.9
• /
• pp.65-87
• /
• 2004

Im $gegew\"{a}rtigen$ Deutschen gibt es zwei verschiedene Auffassungen $\"{u}ber$ das adjektivische Wortbildungsmorphem -Ios. $W\"{a}hrend$ -Ios einerseits als Halbsuffix betrachtet wird, will man es andererseits als Suffix betrachten. Die $Gr\"{u}nde$, die -Ios als Halbsuffix gelten lassen, sind die folgenden : 1. Es besteht eine semantische Verwandtschaft zwischen -Ios und dem freien Morphem Ios. 2. Anders als Suffix hat es ein $zus\"{a}tzliches$ semantisches Merkmal. 3. Die Bildungen mit -Ios haben die Fugenelemente. 4. Es konkurriert mit adjektivischen Halbsuffixen. Die $Gr\"{u}nde$, die -Ios als Suffix gelten lassen, sind die folgenden: 1. Im Unterschied zu -frei und -leer dient es zur wertungsneutralen Feststellung des Sachverhalts 'Nichtvorhandensein'. 2. Es besitzt eine einheitliche semantische Funktion. (BS+ -Ios = ohne BS) 3. Es $geh\"{o}rt$ zur Lautstruktur 'KVK'. Diese entspricht der Lautstruktur der adjektiven Suffixe -bar, -lich. -sam usw. Diese verschiedenen Merkmale von -Ios lassen das Morphem noch nicht als Suffix gelten.

• Annual Conference on Human and Language Technology
• /
• 2014.10a
• /
• pp.149-152
• /
• 2014

자연어처리 분야에서 관계 추출은 중요한 연구 분야이다. 많은 관계 추출 연구는 지도 학습 방법을 사용하지만 정답을 구축하는 비용이 큰 문제가 있다. 본 논문에서는 distant supervision을 이용하여 데이터를 구축하고, suffix tree를 이용한 규칙기반 관계 추출 모델을 제안한다. Suffix tree를 이용한 관계추출의 Macro F1-measure는 84.05%로 관계 추출에서 사용이 가능함을 보였다.

• The Journal of Korean Association of Computer Education
• /
• v.5 no.4
• /
• pp.53-60
• /
• 2002

Typical Apriori-like methods for mining sequential patterns have some problems such as generating of many candidate patterns and repetitive searching of a large database. And PrefixSpan constructs the prefix projected databases which are stepwise partitioned in the mining process. It can reduce the searching space to estimate the support of candidate patterns, but the construction cost of projected databases is still high. For efficient sequential pattern mining, we need to reduce the cost to generate candidate patterns and searching space for the generated ones. To solve these problems, we proposed SuffixSpan(Suffix checked Sequential Pattern mining), a new method for sequential pattern mining, and show a formal approach to our method.

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

• The KIPS Transactions:PartA
• /
• v.15A no.4
• /
• pp.239-242
• /
• 2008

We study the problem of finding the maximum suffix of a string on the external memory model of computation with one disk. In this model, we are primarily interested in designing algorithms that reduce the number of I/Os between the disk and the internal memory. A string of length N has N suffixes and among these, the lexicographically largest one is called the maximum suffix of the string. Finding the maximum suffix of a string plays a crucial role in solving some string problems. In this paper, we present an external memory algorithm for computing the maximum suffix of a string of length N. The algorithm uses four blocks in the internal memory and performs at most 4(N/L) disk I/Os, where L is the size of a block.

• Journal of Korea Society of Industrial Information Systems
• /
• v.15 no.1
• /
• pp.37-46
• /
• 2010

A Suffix Tree is an efficient data structure that exposes the internal structure of a string and allows efficient solutions to a wide range of complex string problems, in particular, in the area of computational biology. However, as the biological information explodes, it is impossible to construct the suffix trees in main memory. We should find an efficient technique to construct the trees in a secondary storage. In this paper, we present a method for constructing a suffix tree in a disk for large set of DNA strings using new index scheme. We also show a typical application example with a suffix tree in the disk.

• Journal of KIISE:Databases
• /
• v.34 no.2
• /
• pp.141-152
• /
• 2007

Selectivity estimation has been a crucial component in query optimization in relational databases. While extensive researches have been done on this topic for the predicates of numerical data, only little work has been done for substring predicates. We propose novel suffix tree transform algorithms for this problem. Unlike previous approaches where a full suffix tree is pruned and then an estimation algorithm is employed, we transform a suffix tree into a suffix graph systematically. In our approach, nodes with similar counts are merged while structural information in the original suffix tree is preserved in a controlled manner. We present both an error-bound algorithm and a space-bound algorithm. Experimental results with real life data sets show that our algorithms have lower average relative error than that of the previous works as well as good error distribution characteristics.