• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.135-141
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• 2004

With the diversified and enlarged softwares, the issue of software maintenance became more complex and difficult and consequently, the cost of software maintenance took up the highest portion in the software life cycle. We design Reverse Engineering Tool for software restructuring environment to object-oriented system. We design Rule - Based Reverse - Engineering using Class Information. We allow the maintainer to use interactive query by using Prolog language. We use similarity formula, which is based on relationship between variables and functions, in class extraction and restructuring method in order to extract most appropriate class. The visibility of the extracted class can be identified automatically. Also, we allow the maintainer to use query by using logical language. So We can help the practical maintenance. Therefore, The purpose of this paper is to suggest reverse engineering tool and evaluation reverse engineering tool.

• Journal of Korean Library and Information Science Society
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• v.40 no.4
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• pp.139-158
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• 2009

This paper used the network analysis method to analyse a variety of attributes of searcher's search behaviors which was appeared on search access log data. The results of this research are as follows. First, the structure of network represented depending on the similarity of the query that user had inputed. Second, we can find out the particular searchers who occupied in the central position in the network. Third, it showed that some query were shared with ego-searcher and alter searchers. Fourth, the total number of searchers can be divided into some sub-groups through the clustering analysis. The study reveals a new recommendation algorithm of associated searchers and search query through the social network analysis, and it will be capable of utilization.

• Interdisciplinary Bio Central
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• v.4 no.4
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• pp.11.1-11.8
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• 2012

Genes that are indispensable for survival are referred to as essential gene. Due to the momentous significance of these genes for cellular activity they can be selected potentially as drug targets. Here in this study, an essential gene for Streptococcus suis was predicted using coherent statistical analysis and powerful genome comparison computational method. At first the whole genome protein scatter plot was generated and subsequently, on the basis of statistical significance, a reference genome was chosen. The parameters set forth for selecting the reference genome was that the genome of the query (Streptococcus suis) and subject must fall in the same genus and yet they must vary to a good degree. Streptococcus pneumoniae was found to be suitable as the reference genome. A whole genome comparison was performed for the reference (Streptococcus pneumoniae) and the query genome (Streptococcus suis) and 14 conserved proteins from them were subjected to a screen for potential essential gene property. Among those 14 only one essential gene was found to be with impressive similarity score between reference and query. The essential gene encodes for a type of 'Clp protease'. Clp proteases play major roles in degrading misfolded proteins. Results found here should help formulating a drug against Strptococcus suis which is responsible for mild to severe clinical conditions in human. However, like many other computational studies, the study has to be validated furthermore through in vitro assays for concrete proof.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1886-1908
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• 2020

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.252-257
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• 2001

In conventional boolean retrieval systems, document ranking is not supported and similarity coefficients cannot be computed between queries and documents. The MMM, Paice and P-norm models have been proposed in the past to support the ranking facility for boolean retrieval systems. They have common properties of interpreting boolean operators softly. In this paper we propose a new soft evaluation method for Information retrieval using query splitting relevance feedback model. We also show through performance comparison that query splitting relevance feedback(QSRF) is more efficient and effective than MMM, Paice and P-norm.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.682-692
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• 2006

Searching for gene products which have similar biological functions are crucial for bioinformatics. Modern day biological databases provide the functional description of gene products using Gene Ontology(GO). In this paper, we propose a technique for semantic similarity search for gene products using the GO annotation information. For this purpose, an information-theoretic measure for semantic similarity between gene products is defined. And an algorithm for semantic similarity search using this measure is proposed. We adapt Fagin's Threshold Algorithm to process the semantic similarity query as follows. First, we redefine the threshold for our measure. This is because our similarity function is not monotonic. Then cluster-skipping and the access ordering of the inverted index lists are proposed to reduce the number of disk accesses. Experiments with real GO and annotation data show that GORank is efficient and scalable.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.33-43
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• 2022

Labeled graphs are used to represent entities, their relationships, and their structures in real data such as knowledge graphs and protein interactions. With the rapid development of IT and the explosive increase in data, there has been a need for a subgraph matching technology to provide information that the user is interested in. In this paper, we propose an approximate Top-k labeled subgraph matching scheme that considers the semantic similarity of labels and the difference in graph structure. The proposed scheme utilizes a learning model using FastText in order to consider the semantic similarity of a label. In addition, the label similarity graph(LSG) is used for approximate subgraph matching by calculating similarity values between labels in advance. Through the LSG, we can resolve the limitations of the existing schemes that subgraph expansion is possible only if the labels match exactly. It supports structural similarity for a query graph by performing searches up to 2-hop. Based on the similarity value, we provide k subgraph matching results. We conduct various performance evaluations in order to show the superiority of the proposed scheme.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2315-2332
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• 1997

In this paper, we implement a content based image retrieval system for image searching by visual features from the image databases on WWW (world wide web). The image retrieval system finds the images that contain the most similar color regions after the system automatically extracts color features from the input image. We can select one of two query methods which use a full image of $4{\times}4$ 16 sketched color region. The image similarity is calculated on the histogram intersection distance and the histogram Euclidean distance. As the experimental results show that the two different query types provide the precision/recall 0.84/0.92 and 0.85/0.93 respectively, this retrieval system has been able to obtain high performance and validity.

• Journal of KIISE:Databases
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• v.27 no.4
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• pp.562-571
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• 2000

이미지 데이터는 텍스트 데이터와는 달리 다양한 색상과 모양, 질감과 같은 비정형적인 특징을 가진다. 따라서 이미지 데이터베이스는 텍스트 기반의 전통 데이터베이스와는 다른 모델링 방법과 질의, 검색 방법을 사용한. 특히, 내용 기반 이미지 검색에서의 검색 속도와 정확도를 향상시키기 위해서는 새로운 복합 질의문 계획 생성 기법이 필요하다. 본 논문에서는 이를 위해 먼저, 단일 조건을 갖는 시각 질의에 대한 처리 기법들을 토대로 여러 조건을 갖는 복합 질의를 처리하기 위한 복합 질의문 계획 생성기법인 SSCC(Similarity Search for Conjunction Combination Query) 알고리즘을 제안한다. SSCC는 이미지 데이터베이스 검색 시스템에서 복합 질의를 처리하기 위한 질의 최적화 과정에서 질의 수행 시간과 투플 I/O를 최소화하는 질의문 계획을 생성하기 위해 사용된다. SSCC 알고리즘은 복합질의를 단일 질의들로 준해하고 퍼지 집합 이론을 도입하여 단일 질의의 결과들을 통합한다. 논문에서 연구된 내용 기반 복합 질의문 계획 생성 기법은 특정 이미지 영역에 국한되지 않으며 다양한 종류의 시각 질의를 수행하기 위한 효율적인 질의문 계획 생성 기법으로 사용될 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.119-124
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• 2007

인터넷 웹사이트의 급격한 증가와 함께 이용자도 증가하고 있으며, 이용 목적은 주로 자료검색과 조회서비스 이다. 조회 요청이 많을수록 질의의 증가를 야기하며, 데이터베이스 서버의 질의 분석(Parse), 질의 실행 계획(Query Execution Plan)을 과도하게 발생시킨다. 즉 데이터베이스 서버에서 처리하는 작업량의 과부하로 인하여 병목현상을 초래한다. 데이터베이스 서버의 조회를 위한 질의처리량을 감소시키는 작업이 필요하다. 그리고 조회 대상이 데이터는 웹사이트에서 자주 갱신되지 않거나, 데이터가 주기적으로 갱신되는 특징이 있다. 이 데이터를 대상으로 데이터베이스 캐쉬 엔진을 구성하면 데이터베이스 서버의 과부하률 해소 할 수 있다. 본 논문에서는 유사 질의 매칭 기반 데이터베이스 캐친 엔진을 설계하고 구현한다. 유사 질의 매칭 기반으로 하여 적중률을 높여 데이터베이스 병목현상을 해결하여, 검색서비스에 더욱 효과적일 것으로 사료되며, 웹사이트의 성능 향상을 기대한다.