• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.234-245
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• 2007

The empirical studies on the implementation of data warehousing systems (DWS) are lacking while there exist a number of studies on the implementation of IS. This study intends to examine the factors affecting the implementation success of DWS. The study adopts the empirical analysis of the sample of 112 responses from DWS practitioners. The study results suggest several implications for researchers and practitioners. First, when the support from top management becomes great, the implementation success of DWS in organizational aspects is more likely. When the support from top management exists, users are more likely to be encouraged to use DWS, and organizational resistance to use DWS is well coped with increasing the possibility of implementation success of DWS. The support of resource increases the implementation success of DWS in project aspects while it is not significantly related to the implementation success of DWS in organizational aspects. The support of funds, human resources, and other efforts enhances the possibility of successful implementation of project; the project does not exceed the time and resource budgets and meet the functional requirements. The effect of resource support, however, is not significantly related to the organizational success. The user involvement in systems implementation affects the implementation success of DWS in organizational and project aspects. The success of DWS implementation is significantly related to the users' commitment to the project and the proactive involvement in the implementation tasks. users' task. The observation of the behaviors of competitors which possibly increases data quality does not affect the implementation success of DWS. This indicates that the quality of data such as data consistency and accuracy is not ensured through the understanding of the behaviors of competitors, and this does not affect the data integration and the successful implementation of DWS projects. The prototyping for the DWS implementation positively affects the implementation success of DWS. This indicates that the extent of understanding requirements and the communication among project members increases the implementation success of DWS. Developing the prototypes for DWS ensures the acquirement of accurate or integrated data, the flexible processing of data, and the adaptation into new organizational conditions. The extent of consulting activities in DWS projects increases the implementation success of DWS in project aspects. The continuous support for consulting activities and technology transfer enhances the adherence to the project schedule preventing the exceeding use of project budget and ensuring the implementation of intended system functions; this ultimately leads to the successful implementation of DWS projects. The research hypothesis that the capability of project teams affects the implementation success of DWS is rejected. The technical ability of team members and human relationship skills themselves do not affect the successful implementation of DWS projects. The quality of the system which provided data to DWS affects the implementation success of DWS in technical aspects. The standardization of data definition and the commitment to the technical standard increase the possibility of overcoming the technical problems of DWS. Further, the development technology of DWS affects the implementation success of DWS. The hardware, software, implementation methodology, and implementation tools contribute to effective integration and classification of data in various forms. In addition, the implementation success of DWS in organizational and project aspects increases the data quality and system quality of DWS while the implementation success of DWS in technical aspects does not affect the data quality and system quality of DWS. The data and systems quality increases the effective processing of individual tasks, and reduces the decision making times and efforts enhancing the perceived benefits of DWS.

• Women's Health Nursing
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• v.4 no.1
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• pp.161-178
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• 1998

In spite of the great progress of the theory and skill of the Nursing Care & Medical area in relation to pregnancy, nurses in clinics face up to many challenges in maternity nursing care areas. The reason is that the mobility and mortality of mothers was sharply decreased and the unknown high-risk diseases of pregnancy woman in the past is made public. That's why it is difficult to meet the pregnancy woman in natural process from pregnancy to delivery in recently. Admission rooms are filled with high-risk pregnancy women. As a matter of fact, we have done nursing care into the surface symptoms and diseases of high-risk pregnancy women so far. We have been indifferent to a long period hospitalization, separation from family, and conflict of repeated examination. Therefore, it is widely spread to understand the emotional conflict experienced by high-risk pregnancy women and to need for nursing intervention to bring up about emotional support and the ability of perception in psychological crisis. Although the pregnancy woman judged in high-risk should carry out normal task of pregnancy, she have to be confronted with secondary risk situation. The health of self & fetus threatened by the risk situation could be decreased through care plan, but psychological stress increases. Therefore, the pregnancy brings into non-control state. It is important to ask that what the hospitalized pregnancy women in high-risk think of themselves status. Because misunderstanding or serious anxiety of themselves status put into mother and fetus in danger. And adaptation mode makes all the difference. I would like to consider how nurses could deal with this high-risk circumstances in the position of pregnancy woman on the basis of the above fact. This study uses phenomenological method to suggest the basis material for nurses to do nursing intervention in view of pregnancy woman. Because this method understands the nature of true life of pregnancy woman throughly. The phenomenological method is the sources to describe or explain affluently the process generated in confirmation areas and environment and is the application for readers to understand and recognize clinic reality and then apply this method to reasoning study place or other places. Specifically, the phenomenon study method, one of the phenomenological method, is applied. The use of that method is to describe and generalize the experience in environment exactly. The study of this study is as follows : Among 187 descriptive stamens from 8 study participants are classified into 42 theme cluster at the stage of the first analysis. Those theme is categorized into 8 sub-subjects such as anxiety of uncertainty, foreknowledge about risk circumstance, will power about overcome, unsettled feeling about hospital, relief, optimistic thought, family support, and indifferences. At the last stage of analysis, those things are categorized into 3 subjects. When high-risk pregnancy woman foretell the situation, they feel unsettlement about uncertainty and untrust feeling about hospital. But they are ease with family support and hospital support. On the other hand, they express indifferent 3-way structure response to the situation having will of overcome and exceeding optimistic thought. In those statements, the experience by pregnancy woman shows 3 respect subjects. 1. They are anxious of this situation and are in desperation and don't recognize their role to be carried out 2. They think of this situation as normal process of pregnancy and are not concerned that this can give themselves and fetus fatal damage. 3. The pregnancy women will never confront this situation. This study shows the pregnancy woman has anxiety and optimistic relief about the situation, and ignores and optimistic relief about the situation, and ignores many things. Therefore, nurses in clinic should give pregnancy woman knowledge and information about the high-risk and help them to deal with the situation spontaneously. High-risk pregnancy woman should have the care plan in respect of the right perception. And the nurse know that their support help out pregnancy woman overcome the crisis in this respect of the special nursing intervention.

• Journal of Christian Education in Korea
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• v.71
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• pp.121-154
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• 2022

This study is to summarize the relationship between youth in terms of developmental psychology, university education, faith, and spirituality in order to form and improve relationships, which are major developmental tasks of youth, and to suggest Christian youth education by the elements of education. Relationships are formed when you are connected to another person and community, feel interested in each other, feel a sense of bond and belonging, and maintain a stable and satisfactory relationship. This is not skill or technology, but is related to life attitude and value, and continuous learning and training are required. Various developmental tasks in youth have something in common with relationships. Relationships positively affect the lives of young people, such as satisfaction with college life in the early stages of youth, adaptation to college life, personality, and career decision. Relationships are also very important in faith because human existence and faith are defined and formed through relationships. The relationship between the community and others plays an important role in spiritual development for the meaning of life and inner growth. In the aspects of learners and educational environment, it was suggested to understand learners with desire for relationships, the generation they live in, and the educational environment in which the relationship between young people occurs. In terms of teachers, teachers have to try to change their roles such as facilitators, guides, managers, and mentors. For the educational purpose and content, it was suggested that relationships should be the ultimate purpose and the educational content for this was presented in three different types of relationships and each main contents to be dealt with. In terms of educational method, it was proposed to select a learner-centered group learning method that induces communication and active participation of learners to cause interaction by considering other elements of education according to the content of the relationship in the cognitive, emotional, and behavioral dimensions. In the aspects of educational results and evaluation, it was proposed to confirm that what was considered during the educational planning stage was effectively carried out in actual education, to evaluate various evaluation methods, various aspects, and to summarize the evaluation results for the specific application.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.