• Review of Korea Contents Association
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• v.15 no.1
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• pp.21-28
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• 2017

• Review of Korea Contents Association
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• v.13 no.1
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• pp.43-47
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• 2015

• Human Ecology Research
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• v.52 no.6
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• pp.573-586
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• 2014

The purpose of this study was to review the conceptual framework of social constructionism and critical social constructionism in the research area of multi-cultural family homes, using a literature review. Fopp argued that social constructionism had an objectivation problem that only considered the actor side as a policy object; therefore he suggested a weaker social constructionist perspective with moderate relativism and the application of feminist epistemology to marginal life for maximizing objectivity. This article explores a conceptual framework for studying the reality of housing of multi-cultural families in Korea in the light of constructionist ideas and presents a review of empirical positivist data to support the framework. Based on results, using the social constructionist framework, five contexts (structural, institutional, organizational, operational, and intersubjective) were reviewed and ideas were suggested to develop an appropriate future situation for multi-cultural family homes. For a weaker social constructionist framework, three National Survey of Multi-Cultural Family Homes data sets were reviewed to determine the real condition of multi-cultural family homes. Further, from a feminist perspective, the empirical data of marginalized multi-cultural family homes were reviewed from the perspectives of gender inequality of decision making, cultural adaptation, and differentiation in housing related areas. In conclusion, two perspectives were useful for understanding multi-cultural family housing in Korea but must be compensated with substantial empirical data for a holistic approach.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.79-86
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• 2002

Recently, it is shown that the unrelated quest ion randomized response models proposed by Moors (1971), Folsom et al.(1973), Greenberg et al.(1971) are in capable of protecting the privacy of the respondent. Thus, in this paper, we review recent days research tendency. Also modification model of Mahmood et al.(1998) is proposed, and we show th at this model is more efficient than Greenberg et al.(1969). Furthermore we treat the privacy protection based on Lanke's (1975) risk of suspicion measure.

• Korea Journal of Hospital Management
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• v.7 no.3
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• pp.121-136
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• 2002

The main purpose of this study is to examine the overall procedure of hospital's internal review of health insurance reimbursement, to present the case of protest against reimbursement cut, and hence to provide some information on hospital's management of medical revenue. The object of the case study is 'P' university medical center, possessing 5 different hospitals under its system. Presentation of the case of protest against reimbursement cut has following meanings: Firstly, to the hospitals that already have internal review departments, information on the details of the protest process and results can be exchanged. Secondly, to the Government and National Health Insurance Corporation, useful data are provided for the improvement of the rules and procedures of health insurance reimbursement. Thirdly, to the hospitals without internal review departments, fundamental materials on the internal review process are provided for the effective management of medical revenue.

• 한국벤처창업학회:학술대회논문집
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• 2022.04a
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• pp.123-126
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• 2022

Online reviews offer valuable information to businesses by reflecting consumer experiences about their products and services. Two important aspects of online reviews are first, the topics consumers choose to address and second, the sentiments expressed in their reviews. Building upon previous literature that shows online reviews are context-dependent, we examine the impact of topic distribution on review sentiment in South Korea and the U.S. during pre-and post-pandemic periods. After performing topic modeling on Airbnb app review data, we measure the contribution of each topic on review sentiment using SHAP values. Our results indicate variations in topic distribution trends between 2018 and 2021. Also, the order and magnitude of topics' impact on review sentiment change between pre-and post-pandemic periods for both countries. This study can help businesses to understand how topics and sentiments associated with their products and services changed after pandemic, and also help them identify areas of improvement.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.514-536
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• 2022

Online reviews offer valuable information to businesses by reflecting consumer experiences about their products and services. Two crucial aspects of online reviews are the topics consumers choose to address, and the sentiments expressed in their reviews. Building upon previous literature that shows online reviews are context-dependent, we employ the Expectation-Confirmation Theory (ECT) to examine the impact of topic distribution on review sentiment in South Korea and the U.S. during pre- and post-pandemic periods. After applying a topic modeling to Airbnb app review data, we measure the contribution of each topic on review sentiment using SHAP values. Our results indicate variations in topic distribution trends between 2018 and 2021. In addition, the order and magnitude of topics' impact on review sentiment change between pre- and post-pandemic periods for both countries. This study can help businesses understand how topics and sentiments associated with their products and services changed after the pandemic and thus identify areas of improvement.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.220-230
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• 2013

In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control.

• Molecules and Cells
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• v.25 no.2
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• pp.279-288
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• 2008

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.