Contribution to Improve Database Classification Algorithms for Multi-Database Mining

Salim Miloudi,Sid Ahmed Rahal,Salim Khiat 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.3

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact,when a multi-branch company needs to explore its distributed data for decision making, it is imperative toclassify these multiple databases into similar clusters before analyzing the data. To search for the bestclassification of a set of n databases, existing algorithms generate from 1 to (n2–n)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification aresubsets of clusters in the next classification), existing algorithms generate each classification independently,that is, without taking into account the use of clusters from the previous classification. Consequently, existingalgorithms are time consuming, especially when the number of candidate classifications increases. Toovercome the latter problem, we propose in this paper an efficient approach that represents the problem ofclassifying the multiple databases as a problem of identifying the connected components of an undirectedweighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of ouralgorithm against existing works and that it overcomes the problem of increase in the execution time.

Contribution to Improve Database Classification Algorithms for Multi-Database Mining

Miloudi, Salim,Rahal, Sid Ahmed,Khiat, Salim Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.3

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

2D 라이다 데이터베이스 기반 장애물 분류 기법

이무현,허수정,박용완,Lee, Moohyun,Hur, Soojung,Park, Yongwan 대한임베디드공학회 2015 대한임베디드공학회논문지 Vol.10 No.3

We propose obstacle classification method based on 2D LIDAR(Light Detecting and Ranging) database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width, intensity, variance of range, variance of intensity data. The first classification was processed by the width data, and the second classification was processed by the intensity data, and the third classification was processed by the variance of range, intensity data. The classification was processed by comparing to database, and the result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.

상품 데이터베이스의 동적 특성을 지원하는 분류 모형

김동규 ( Dong Kyu Kim ),이상구 ( Sang Goo Lee ),최동훈 ( Dong Hoon Choi ) 한국정보처리학회 2005 정보처리학회논문지D Vol.12 No.1

A product classification scheme is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eCl@ss, however, have a lot of limitations to meet these requirements for dynamic features of classification. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes, and describe the semantic classification model proposed in [1], which satisfies the requirements for dynamic features of product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph.

『風騷軌範』 소재 동파시의 분류 연구—『增刊校正王壯元集註分類東坡先生詩』와의 비교를 중심으로—

지영원 고전문학한문학연구학회 2023 고전과 해석 Vol.41 No.-

이 글은 조선 전기 詩選集인 『風騷軌範』 전후집에 수록된 蘇軾의 시와 그 분류양상을 이 무렵 수입된 분문찬류 형태의 시집 『增刊校正王狀元集註分類東坡先生詩』와 비교 분석하여 풍소궤범에 내재된 분류의식이 가진 차별점과 그 의미를 살펴보는 데 목적을 두었다. 먼저 『풍소궤범』에 수록된 소식 시의 특징적인 부분들을 확인하였다. 그 구체적인 방법으로는 『풍소궤범』에서 소식의 시가 수록된 실질 양상을 정리하여 이를 바탕으로 여러 눈에 띄는 부분들을 살펴보았다. 특히 제목, 내용, 시 수록의 층차에서 비롯되는 특이점들을 살펴 이것이 시사하는 바를 확인하였다. 확인 결과 『풍소궤범』은 여느 고전 시선집과 마찬가지로 수록 층차의 곳곳에 선자의 의도가 표출되어 있었으며, 특히 시들 간의 상호 관계와 맥락을 중요하게 여긴 사실을 확인할 수 있었다. 다음으로 같은 주제별 분류 양식을 사용하고 있는 『풍소궤범』 후집과 『증간교정왕장원집주분류동파선생시』의 분류 형태를 비교하는 방식으로 선자의 의도와 분류의식, 문학적 경향성을 살펴보았다. 이를 통해 비교적 포괄성을 줄여 분류 기준을 간명화하면서도 일부 분류에 대해서는 뚜렷한 주관성을 드러내었던 면모를 열람할 수 있었다. 본 연구는 방대한 자료량과 그 증빙의 여파로 논의할 만한 부분 중 작은 단면만을 조망하는 데 그치고 있지만, 이러한 시도는 차후 한시 데이터베이스와 그 주제분류를 논의하는 데 초석이 될 수 있을 것이다. This article aims to analyze the classification patterns of 蘇軾’s poems in the 『風騷軌範』, compared to 『增刊校正王狀元集註分類東坡先生詩』. First, examined the microscopic details of editorialization in the 『風騷軌範』 collections that follow the formal classification. This exposes the writers of 『風騷軌範』’s tendency for classification, which is discreetly displayed in the specifics of their deviations. Subsequently, looked at the areas of the 『風騷軌範』’s general classification that deal with topic categorization and include complex intentions. In particular, diagnosed the tendency by focusing on the contents that clearly show the intention of the author in the hierarchy. Next, looked at the differences in the actual classification of 『風騷軌範』 and 『增刊校正王狀元集註分類東坡先生詩』. Through this, examined the classification consciousness and literary tendencies of 『風騷軌範』. By reducing the relatively comprehensive parts, 『風騷軌範』 not only able to simplify the classification criteria, but also able to see the distinct subjectivity of some classifications. While this study only scratches the surface of the phenomenon due to the lack of references, this attempt can serve as a foundation for discussing the classification of Sino-Korean Poetry, which is clearly a necessary part of constructing a database of Sino-Korean Poetry.

무용가 개인컬렉션의 데이터베이스(DB) 구축을 위한 춤자료 분류체계 연구 : 김천흥컬렉션을 중심으로

유시현,권혜경,김현주,최해리 한국무용기록학회 2009 무용역사기록학 Vol.17 No.-

무용가 개인컬렉션의 데이터베이스(DB) 구축을 위한 춤자료의 분류체계 개발은 자료를 분류하고 정리하는 기존의 목록화(cataloging) 작업에서 더 나아가 이용자의 자료 검색 환경을 최적화하는데 그 목적이 있다. 따라서 본 논문은 김천흥컬렉션을 대상으로 춤자료의 DB구축 및 일반이용자들이 춤자료를 검색하고 사용할 수 있는 검색시스템을 개발하는 것을 목적으로 하였다. 이를 위해 본 연구는 우선 국내외 춤 자료관들을 대상으로 춤자료의 보유 현황 및 분류 현황을 살펴보고 춤자료 분류방식을 크게 세 가지로 구분하였다. 첫째는 자료의 저장 방식이나 저장 형식, 자료의 형식과 양식 등 매체가 갖고 있는 특징에 따라 자료의 형태(format)를 중심으로 한 분류이다. 둘째는 자료가 가진 내용을 토대로 자료가 생성된 특정 맥락에 중점을 두고 분류하는 자료의 맥락적(context) 분류이다. 셋째는 특별한 체계 없이 자료가 가진 공통적 속성을 바탕으로 동일한 분류항목으로 묶어 자료를 정리하는 방법이다. 이 중 자료를 분류하고 검색하는데 상대적으로 용이한 "형태 중심 분류법"을 김천흥컬렉션 데이터베이스(DB) 구축의 기본 틀로 정하였으며 이를 바탕으로 김천흥의 춤자료를 총 11개의 카테고리로 분류하였다. 또한 자료의 형태 중심 분류법이 놓치기 쉬운 자료의 생성 맥락과 타자료와의 연관성에 관한 정보는 상세정보들을 제시할 수 있는 필드(fields)를 구성하여 보충하였다. 이는 자료의 형태적 분류가 갖는 자료의 비맥락화에 대한 단점을 부분적으로 보충하고자 한 결과이다. 일단 데이터화 된 정보들은 검색엔진을 통해 주제어를 중심으로 다양한 자료 형태를 아우르는 통합검색 또한 가능하게 하였으며, 결과적으로 "카테고리별 상세검색"과 "통합검색"의 두 가지 방식으로 자료에 대한 횡적, 종적 접근을 모두 가능하게 하였다. 본 연구 결과는 김천흥이라는 특정 인물의 자료를 바탕으로 설계된, 즉 김천흥컬렉션에 최적화 된 시스템이며, 이후 타 예술인의 컬렉션을 형성하면서 끊임없는 수정과 보완을 거쳐 한국춤 전반에 관한 자료들을 통합적으로 분류, 검색할 수 있는 DB 시스템으로 발전해 가야 할 것이다. The development of archiving dance materials to build a DB of individual dancer's collection is to optimize users' search environment, developed from the old cataloging work, or classifying and arranging materials. Therefore, this article intends to build a DB and develop a search engine for Kim Cheon-heung Collection. For this purpose, this article examines dance materials and classifying systems of Dance Centers both in Korea and abroad, and summarized the system as three categories. The first system is the "format classification" in which materials are categorized by their format such as data storage method, storage format and forms and patterns of materials. The second one is "contextual classification" focusing on specific context based on the context of materials. The third one is the method to classify materials by same categories based on common characteristics without applying specific systematic method. Among these, "format classification system" was taken as a basic structure for Kim Cheon-heung Collection, which is relatively easy to classify and search materials, and Kim Cheon-heung Collection was classified by 11 categories. Also, the information of the history of the materials and the relationship with other materials, which is easily missed in format classification system, was supplemented by fields, which could present detailed information. This is to make up for the shortcomings of incoherence of the materials in format classification system. Data is accessible by general search engine with subject words, consequently it was made possible to access materials horizontally and vertically by "category search," and "general search." This study is optimized for Kim Cheon-heung Collection designed for the materials related to Kim Cheon-heung, and it should be developed into the DB system that could classify and search general Korean dance materials through incessant modification and complement in the process of building other collections.

Evaluation of 16S rRNA Databases for Taxonomic Assignments Using a Mock Community

Park, Sang-Cheol,Won, Sungho Korea Genome Organization 2018 Genomics & informatics Vol.16 No.4

Taxonomic identification is fundamental to all microbiology studies. Particularly in metagenomics, which identifies the composition of microorganisms using thousands of sequences, its importance is even greater. Identification is inevitably affected by the choice of database. This study was conducted to evaluate the accuracy of three widely used 16S databases-Greengenes, Silva, and EzBioCloud-and to suggest basic guidelines for selecting reference databases. Using public mock community data, each database was used to assign taxonomy and to test its accuracy. We show that EzBioCloud performs well compared with other existing databases.

Data-processing pipeline and database design for integrated analysis of mycoviruses

제미경,손현석,김하연 한국인터넷방송통신학회 2019 Journal of Advanced Smart Convergence Vol.8 No.3

Recent and ongoing discoveries of mycoviruses with new properties demand the development of an appropriate research infrastructure to analyze their evolution and classification. In particular, the discovery of negative-sense single-stranded mycoviruses is worth noting in genome types in which double-stranded RNA virus and positive-sense single-stranded RNA virus were predominant. In addition, some genomic properties of mycoviruses are more interesting because they have been reported to have similarities with the pathogenic virus family that infects humans and animals. Genetic information on mycoviruses continues to accumulate in public repositories; however, these databases have some difficulty reflecting the latest taxonomic information and obtaining specialized data for mycoviruses. Therefore, in this study, we developed a bioinformatics-based pipeline to efficiently utilize this genetic information. We also designed a schema for data processing and database construction and an algorithm to keep taxonomic information of mycoviruses up to date. The pipeline and database (termed ‘mycoVDB’) presented in this study are expected to serve as useful foundations for improving the accuracy and efficiency of future research on mycoviruses.

자동차 주행환경에서 보행자 분류를 위한 딥러닝 모델의 전이학습 및 성능비교

변영현(Yeong-Hyeon Byeon),곽근창(Keun-Chang Kwak) 한국정보기술학회 2018 한국정보기술학회논문지 Vol.16 No.10

In this paper, a performance comparison of deep-learning models for pedestrian classification under automobile driving environment is performed. Most automobiles nowadays are equipped with black boxes, and driver assistance systems are also applied to camera based image processing technologies. Pedestrian classification plays an important role in determining the final decision whether a candidate region is a person or not. We perform the transfer learning based on AlexNet, GoogLeNet, and ResNet that are well known as deep-learning models. For comparison experiments of the deep learning models, we used INRIA database and Chosun University (CU) database constructed under automobile driving environment. The INRIA training data set is used for transfer learning and performance validation is used with INRIA testing data set and CU database. The experimental results showed that the performance of ResNet based on transfer learning outperformed AlexNet and GoogLeNet.

Data-processing pipeline and database design for integrated analysis of mycoviruses

Je, Mikyung,Son, Hyeon Seok,Kim, Hayeon The Institute of Internet 2019 International journal of advanced smart convergenc Vol.8 No.3

Recent and ongoing discoveries of mycoviruses with new properties demand the development of an appropriate research infrastructure to analyze their evolution and classification. In particular, the discovery of negative-sense single-stranded mycoviruses is worth noting in genome types in which double-stranded RNA virus and positive-sense single-stranded RNA virus were predominant. In addition, some genomic properties of mycoviruses are more interesting because they have been reported to have similarities with the pathogenic virus family that infects humans and animals. Genetic information on mycoviruses continues to accumulate in public repositories; however, these databases have some difficulty reflecting the latest taxonomic information and obtaining specialized data for mycoviruses. Therefore, in this study, we developed a bioinformatics-based pipeline to efficiently utilize this genetic information. We also designed a schema for data processing and database construction and an algorithm to keep taxonomic information of mycoviruses up to date. The pipeline and database (termed 'mycoVDB') presented in this study are expected to serve as useful foundations for improving the accuracy and efficiency of future research on mycoviruses.