• Journal of Korean Academy of Nursing Administration
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• v.9 no.3
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• pp.415-427
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• 2003

Purpose: The purpose of this study was to investigate the degree and pattern of knowledge management process, and to identify the relationship between knowledge management process and organizational effectiveness in clinical nurses. Method: Participants were 665 regular clinical nurses who had worked for over 1 year in general units of 9 tertiary medical hospitals including 2 national university hospitals, 5 university hospitals, and 2 hospitals founded by business enterprises. Data were collected from March to May 2003 through questionnaires. Four structured instruments were used to collect the data: Knowledge Management Process Scale(Jeong, Lee, Lee, & Kim, 2003), cCommitment Questionnaire(Mowday, Steers, & Porter, 1979), General Satisfaction Scale(CooK, Hepworth, Wall, & Warr, 1981), and one for general characteristics. The data were analyzed using factor analysis, reliability analysis, descriptive analysis, cluster analysis, one-way ANOVA, Scheffe test, correlation analysis with the SPSS for Windows 10.0 program. Result: 1) The average score for knowledge management process in nurses was $3.08{\pm}.54$ on a 5-point Likert scale. In order from highest mean score, the elements of knowledge management process, were Knowledge $Utilization(3.35{\pm}.57)$, Knowledge $Sharing(3.07{\pm}.58)$, Knowledge $Creation(2.99{\pm}.63)$, and Knowledge $Storage(2.91{\pm}.82)$. 2) Four knowledge management patterns for nurses, which were derived from cluster analysis, were inactivate pattern, delayed pattern, activate pattern, and high-activate pattern of knowledge management. 3) The degree of knowledge management process activation and 4 elements of knowledge management process, Knowledge Creation, Knowledge Storage, Knowledge Sharing, and Knowledge Utilization, were significantly correlated with nurses' organizational commitment and job satisfaction(p=.000). 4) The nurses' organizational commitment and job satisfaction showed significant differences according to the knowledge management patterns derived from cluster analysis of high-activate pattern, activate pattern, delayed pattern, inactivate pattern(p=.000). Conclusion: These results suggest that there are four knowledge management patterns for nurses, and knowledge management process positively affects the nurses' organizational commitment and job satisfaction. From the above findings, knowledge management process is empirically verified as a useful and effective method to increase organizational effectiveness, and develop the organization.

• Journal of Korean Elementary Science Education
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• v.23 no.1
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• pp.61-73
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• 2004

The purpose of this study was to suggest a grounded theory on the process of undergraduate students' generating pattern-knowledge about scientific episodes. The pattern-discovery tasks were administered to seven college students majoring in elementary education. The present study found that college students show five types of procedural knowledge represented in the process of pattern-discovery, such as element, elementary variation, relative prior knowledge, predictive-pattern, and final pattern-knowledge. Furthermore, subjects used seven types of thinking ways, such as recognizing objects, recalling knowledges, searching elementary variation, predictive-pattern discovery, confirming a predictive-pattern, combining patterns, and selecting a pattern. In addition, pattern-discovering process involves a systemic process of element, elementary variation, relative prior knowledge, generating and confirming predictive-pattern, and selecting final pattern-knowledge. The processes were shown the abductive and deductive reasoning as well as inductive reasoning. This study also discussed the implications of these findings for teaching and evaluating in science education.

• STI Policy Review
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• v.2 no.1
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• pp.55-73
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• 2011

Intermediaries are presenting not only transactional information that orchestrates electronic transaction, but also market related information and knowledge useful for sellers' understanding of the market status. However, we do not have strong evidence that the market related information and knowledge is properly utilized by sellers and that it has an actual influence on sellers' performance. According to the research result, absorptive capacity and the pattern of information system utilization is statistically significant to sellers' performance mediated by operational efficiency and market knowledge creation. Especially, explorative utilization of information system and realized absorptive capacity has a stronger influence on sellers' performance mediated by market knowledge creation. With this research result, this study maintains that sellers are required to absorb and utilize market related information and knowledge more actively through explorative utilization of information system to achieve better performance in the e-marketplace. On the other hand, intermediaries are recommended to provide abundant and valuable market related information and knowledge for the sellers to build up better e-marketplaces.

• Journal of Physiology & Pathology in Korean Medicine
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• v.26 no.6
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• pp.818-822
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• 2012

We in this paper propose a prognosis graph, analyzing prognoses of each pattern described in the Korean medicine literatures. This graph is represented as the integrated graphs about knowledge of patterns and their transitions in the prognoses, where a node becomes a pattern name and a edge becomes a transition between patterns, along with a condition with respect to cause or mechanism of the pattern. The knowledge of prognoses which a pattern is transit into another pattern can be identified at a glance by using this model. We also construct a upper-level prognosis graph, excluding five viscera and six entrails from the model. This upper-level prognosis graph contains the conceptual knowledge than clinical one so that it may be helpful to students and researchers in the Korean medicine fields.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.731-740
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• 1991

This paper presents a knowledge based system to solve reactive power/voltage control problem in a power system. The methods to reduce inference time are proposed in inferring the solution of problem in the knowledge base which consists of heuristic rules and inowledge of experts. A set of indices drawn from the heuristic knowledge on the power system is utilized to make up for the defect of existing knowledge based systems which determine both the location and the amount of reactive power compensation devices. The concept of set of indices developed in this paper makes it possible to infer the amount of reactive power source only since the bus order list representing priority for the location of reactive power compensator to be switched on can be determined in advance. From the fact that there exists a relationship between the system voltage pattern and the reactive power pattern in operation, the pattern recognition technique is introduced to reduce the inference time in solving the severe voltage problem. To demonstrate the usefulness of the proposed knowledge based system, the IEEE 30 bus system is chosen as a sample system. The results of case study are also presented.

• Knowledge Management Research
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• v.5 no.2
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• pp.25-51
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• 2004

The purpose of this study is to investigate the knowledge creating pattern of technology catching-up and pioneering phase in the new product development process. This paper first reviews the knowledge conversion, absorptive capability, learning orientation and the trigger of learning. The paper then presents the integrative model of knowledge creating. Based on the integrative model, in-depth case analysis was conducted on the new product development process in Phicom. As a result, the paper discuss that the pattern of absorptive capability building, knowledge conversion and knowledge transfer is different from technology catching-up and pioneering phase. Finally, the implications and limitations of the study are discussed.

• Journal of Technology Innovation
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• v.7 no.2
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• pp.119-144
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• 1999

Recently, knowledge management(KM) has attracted increasing attention from academicians and practitioners alike. Amongst others, technological knowledge(TK) is considered principal asset of KM and R,&D organization of private firms selves as primary actor of KM. It is also noted that the notion of sectoral pattern of innovation highlights idiosyncratic differences across industrial sectors in terms of TK management. That is, knowledge contents, knowledge generation and How pattern are considerably different among industries. This paper first analyzes the correlation between structural of R&D organization and industrial(sectoral) type to identify the dominant structure of R&D organization for each industry. Second, sector-specific architectures of TK management system are proposed. According to structural characteristic of R&D-organization type, test-practice forms of TK management system are suggested in terms of such factors as knowledge contents(technology information), knowledge generation activities, and knowledge storage/retrieval modes.

• Journal of Information Technology Applications and Management
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• v.26 no.1
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.

• Journal of Technology Innovation
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• v.8 no.1
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• pp.73-94
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• 2000

The main objective of this research is to examine the spillover effects of technological knowledge from IT industry to other industrial sectors and, based on empirical findings, to draw policy implications and suggest policy directions. To this end, we divide IT industry into IT equipment and IT service, assuming that these two sub-sectors are considerably different each other in terms of technology knowledge flow. Other industries are classified into 17 different sectors based on the KSIC of 1990. As the proxy measure of technological knowledge, the notion of R&D stock is employed. The Input/output(I/O) Table is used to define the inter-industrial flow pattern and to draw the knowledge flow matrix. As the research methodology, cost function model is employed to gauge the spillover effects of technological knowledge of IT industry. Based on the results of analysis, it is found that the economic impact of technology diffusion also exhibits a different pattern between IT equipment and IT service. The diffusion of IT equipment tends to show labor-substitution effect whereas IT service displays labor-creation effect. This fact should be considered in devising industry, education, and labor policy. The expectations from this research are as follows. First, the sectoral pattern, difference between IT equipment and service in particular, identified from this research may shed light on the sector-specific policy direction. It is emphasized that a sector-specific approach, rather than an aggregate approach, is relevant for formulating IT policy. Second, it is expected that the importance of technology diffusion programs and policy measures are recognized among policy makers in IT industry.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1103-1114
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• 2003

Currently, one of the most critical issues in developing the service support system for various spatio-temporal applications is the discoverying of meaningful knowledge from the large volume of moving object data. This sort of knowledge refers to the spatiotemporal moving pattern. To discovery such knowledge, various relationships between moving objects such as temporal, spatial and spatiotemporal topological relationships needs to be considered in knowledge discovery. In this paper, we proposed an efficient method, MPMine, for discoverying spatiotemporal moving patterns. The method not only has considered both temporal constraint and spatial constrain but also performs the spatial generalization using a spatial topological operation, contain(). Different from the previous temporal pattern methods, the proposed method is able to save the search space by using the location summarization and generalization of the moving object data. Therefore, Efficient discoverying of the useful moving patterns is possible.