• Journal of the Korean Society for information Management
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• v.24 no.1 s.63
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• pp.121-145
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• 2007

This study aims to identify copyright issues regarding the knowledge content currently circulated through knowledge exchange markets in the Republic of Korea. The content providers of knowledge exchange markets comprise government & public institutions, full-text database companies, publishers and individuals. It is worth noting that commercial trade of copyrighted content or material among academic journals, database companies and knowledge exchange markets essentially exclude individual authors who are the actual copyright holders. In principle, the original author owns the copyright whether it has an explicit notice or not. Unless the author/owner officially agrees to transfer the copyright including the right for so-called "derivative works", content-making based or derived from the copyrighted material, digitalization of the copyrighted work as well as its registration on full-text database and circulation through knowledge markets are illegal.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2863-2874
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• 1999

Apartment auction is a system that is used for the citizens to get a house. This paper deals with the implementation of a web-based intelligent decision support system using OLAP technique and data mining technique for auction decision support. The implemented decision support system is working on a real auction database and is mainly composed of OLAP Knowledge Extractor based on data warehouse and Auction Data Miner based on data mining methodology. OLAP Knowledge Extractor extracts required knowledge and visualizes it from auction database. The OLAP technique uses fact, dimension, and hierarchies to provide the result of data analysis by menas of roll-up, drill-down, slicing, dicing, and pivoting. Auction Data Miner predicts a successful bid price by means of applying classification to auction database. The Miner is based on the lazy model-based classification algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm to reflect the characteristics of auction database.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.102-106
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• 1997

Grinding has difficulty in satisfying the qualitative knowledge based on the skilled expert as well as quantitative data for all user. Design of grinding database is based on the frame-based model for utilizing the empirical and qualitative knowledge. Inthis paper, basic strategy to develop the grinding database by frame-based model, which is strongly dependent upon experience and intuition, frame-base model, which is strongly dependent upon experience and intuition, is described. Design of grinding database is based on the frame-based model for utilizing the ambiguous knowledge and inference is accomplised by the object-oriented paradigm system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.101-112
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• 1998

Data mining, which is also referred to as knowledge discovery in database, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases. The discovered knowledge can be applied to information management, decision making, and many other applications. In this paper, a new data mining problem, discovering sequential patterns, is proposed which is to find all sequential patterns using sampling method. Recognizing that the quantity of database is growing exponentially and transaction database is frequently updated, sampling method is a fast algorithm reducing time and cost while extracting the trend of customer behavior. This method analyzes the fraction of database but can in general lead to results of a very high degree of accuracy. The relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time. The superiority of the proposed algorithm will be shown through analyzing accuracy and efficiency by comparing with Apriori All algorithm.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.1
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• pp.341-360
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• 2015

This paper proposes a noble service architecture based on scientific infographic as well as semi-automatic knowledge process for 'KISTI's Scent of Science' database, which has been highly credited as a science journalism service in Korea. Unlike other specialized scientific databases for domain experts and scientists, the database aims at providing comprehensible and intuitive information about various important scientific concepts which may seem not to be easily understandable to general public. In order to construct a knowledge-base from the database, we deeply analyze the traits of the database and then establish a semi-automatic approach to identify and extract various scientific intelligence from its contents. Furthermore, this paper defines a scientific infographic service platform based on the knowledge-base by offering its detailed structure, methods and characteristics, which shows a progressive future direction for science journalism service.

• The Journal of Korean Association of Computer Education
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• v.9 no.2
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• pp.47-55
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• 2006

To determine efficient methods of database teaching, this paper applied the situated learning theory to the teaching and learning method and analyzed the effects. Previous related studies were also examined, with the essential contents in database analyzed based on Bloom's taxonomy of educational objectives. Moreover, this paper presented a strategy wherein the contents of database learning are classified into two categories: basic knowledge and technical and extended knowledge. Experimental and control groups were selected based on related studies, and the effects of database teaching and learning method, determined by technique and attitude area as well as knowledge area. After preparing and applying to the teaching and learning method the actual educational curriculum, the following results were drawn: (1) the experimental group showed better performance in terms of understanding the concept of database, operating database, and constructing a database table when the situated learning theory was applied to the teaching method, and; (2) the experimental group was also more receptive compared to the control group, which opted to take technique-oriented database courses. Therefore, various teaching and learning methods aside from the situated learning theory should be applied and analyzed in database and computer science fields for maximum effects.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.6
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• pp.183-193
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• 2003

Most construction works on the soft ground adopt instrumentation to manage settlement and stability of the embankment. The rapid progress of the information technologies and the digital data acquisition on the soft ground instrumentation has led to the fast-growing amount of data. Although valuable information about the behaviour of the soft ground may be hiding behind the data, most of the data are used restrictedly only for the management of settlement and stability. One of the critical issues on soft ground instrumentation is the long-term settlement prediction. Some observational settlement analysis methods are used for this purpose. But the reliability of the analysis results is remained in vague. The knowledge could be discovered from a large volume of experiences on the observational settlement analysis. In this article, we present a database to store settlement records and data mining procedure. A large volume of knowledge about observational settlement prediction were collected from the database by applying the filtering algorithm and knowledge discovery algorithm. Statistical analysis revealed that the reliability of observational settlement analysis depends on stay duration and estimated degree of consolidation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.290-293
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• 2000

Knowledge discovery refers to the overall process of discovering useful knowledge from data. The linear programming model is a special form of useful knowledge that is embedded in a database. Since formulating models from scratch requires knowledge-intensive efforts, knowledge-based formulation support systems have been proposed in the DSS area. However, they rely on the strict assumption that sufficient domain knowledge should already be captured as a specific knowledge representation form. Hence, the purpose of this paper is to propose a methodology that finds useful knowledge on building linear programming models from a database. The methodology consists of two parts. The first part is to find s first-cut model based on a data dictionary. To do so, we applied the GPS algorithm. The second part is to discover a second-cut model by applying neural network technique. An illustrative example is described to show the feasibility of the proposed methodology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.111-114
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• 2004

Management of human knowledge is an interesting concept that has attracted the attention of philosophers for thousands of years. Artificial intelligence and knowledge engineering has provided some degree of rigor to the study of knowledge systems and expert systems(ES) re able to use knowledge to solve the problems and answer questions. Therefore, the process of conceptualization and inference of knowledge are fundamental problem solving activities and hence, are essential activities for solving the problem of software ES construction Especially, the access to relevant, up-to-date and reliable knowledge is very important task in the daily work of physicians and nurses. In this study, we propose the conceptualization and inference mechanism for implicit knowledge management in medical diagnosis area. To this purpose, we combined the dynamic knowledge map(KM) and relational database(RDB) into a dynamic knowledge map(DKM). A graphical user-interface of DKM allows the conceptualization of the implicit knowledge of medical experts. After the conceptualization of implicit knowledge, we developed an RDB-based inference mechanism and prototype software ES to access and retrieve the implicit knowledge stored in RDB. Our proposed system allows the fast comfortable access to relevant knowledge fitting to the demands of the current task.

• Genomics & Informatics
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• v.3 no.2
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• pp.47-51
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• 2005

Molecular mechanisms of biological processes are typically represented as 'pathways' that have a graph­analogical network structure. However, due to the diversity of topics that pathways cover, their constituent biological entities are highly diverse and the semantics is embedded implicitly. The kinds of interactions that connect biological entities are likewise diverse. Consequently, how to model or process pathway data is not a trivial issue. In this review article, we give an overview of the challenges in pathway database development by taking the INOH project as an example.