• Journal of Families and Better Life
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• v.14 no.2
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• pp.35-50
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• 1996

This paper is a review and assessment of 1122 articles on families in later life from 5 journals published during 1980 and 1995. theoretical and methodological issues family relationships in later life social support network caregiving attitudes and stress life satisfaction and stress of the aged and welfare system for the aged are the major areas examined. The review indicates that this area of research in quantity has increased rapidly since 195. Relationships between aged parents and their adult children focusing on caregiving attitudes behavior and stresses for supporting their parents among the children and adjustment/life satisfaction of the aged are dominantly investigated. However most of the research are non-theoretical and descriptive in nature and the influences of socio-economic variables such as sex health economic status and educational level on dependent variables are widely investigated. Future research questions and issues under the sub-ares of families in lat r life is provided. Further directions and suggestions for future research works on families in later life in general are provided with particular emphasis on conceptual and methodological issues.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1286-1287
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• 2012

자원을 효율적으로 관리할 수 있는 분산 처리 분야의 활용도가 높아지고 있는 가운데 P2P 기술의 유용성이 분산 환경에서 입증되고 있다. 이러한 P2P 시스템에서 다양한 컨텐츠를 이용하여 정보 제공의 역할을 하는 서버와 피어들로 구성된 P2P 시스템은 일반적인 자원 보유 서버로 구성된 시스템에 비해 서버의 부하가 적은 것이 사실이다. 본 논문에서는 식당에서 음식 메뉴를 제공함에 있어서 소셜 네트워크 기반의 공동 구매에 있어 개선된 정보와 연계되어 효과적으로 진행되는 P2P 시스템을 제안한다. 소셜 네트워크 기반의 공동 구매 정보는 P2P 시스템을 이용하는 피어에 의해 제공되고, 이러한 중계 정보는 P2P 서버에 의해 제공되는 형태이다.

• Korean Association of Arts Management
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• no.58
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• pp.153-177
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• 2021

This study attempted to analyze language networks based on the theory of art policy in the New Normal era triggered by COVID-19 and domestic and foreign policy trends. For analysis, data containing key words of "Corona" and "Art" were collected from Google News and Web documents from March to September 2020 to extract 227 refined subject words, and the extracted subject words were analyzed as indicators of frequency and centrality of subject words through the Netminor program. In addition, visualization analysis of semantic networks has been attempted for the analysis of relationships between each topic languages. As a result of the semantic network analysis, the most frequent topic was "Corona," and "Culture and Art," "Art," "Performance," "Online" and "Support" were included in the group with the most frequencies. In the centrality analysis, "Corona" was the most popular, followed by "the era," "after," "post," "art," and "cultural arts," with high frequency, "Corona," "art," and "cultural arts" also dominated most centrality. In particular, the top-level key words in the analysis of frequency and centrality of the topic are 'online' and 'support' and 'policy'. This can be seen as indicating that the rapid rise of non-face-to-face and online content and support policies for the artistic communities are needed due to the dailyization of social distance due to COVID-19.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.2
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• pp.175-185
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Gestational diabetes mellitus (GDM) and Breastfeeding' field of research for better understanding research trends in the past 20 years. Methods: This was a text-mining and topic modeling study composed of four steps: 1) collecting abstracts, 2) extracting and cleaning semantic morphemes, 3) building a co-occurrence matrix, and 4) analyzing network features and clustering topic groups. Results: A total of 635 papers published between 2001 and 2020 were found in databases (Web of Science, CINAHL, RISS, DBPIA, RISS, KISS). Among them, 3,639 words extracted from 366 articles selected according to the conditions were analyzed by text network analysis and topic modeling. The most important keywords were 'exposure', 'fetus', 'hypoglycemia', 'prevention' and 'program'. Six topic groups were identified through topic modeling. The main topics of the study were 'cardiovascular disease' and 'obesity'. Through the topic modeling analysis, six themes were derived: 'cardiovascular disease', 'obesity', 'complication prevention strategy', 'support of breastfeeding', 'educational program' and 'management of GDM'. Conclusion: This study showed that over the past 20 years many studies have been conducted on complications such as cardiovascular diseases and obesity related to gestational diabetes and breastfeeding. In order to prevent complications of gestational diabetes and promote breastfeeding, various nursing interventions, including gestational diabetes management and educational programs for GDM pregnancies, should be developed in nursing fields.

• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.1-28
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• 2017

Today, according to the increased importance of industry-university cooperation in the knowledge-based economy, support and the number of researches involved in industry-university cooperation has also steadily increased. But it is true that profits from the outcome of patents resulting from such cooperation, such as technology transfer and royalty fees, are lower than they are supposed to be, because of excessive patents applications, although some of them have little commercial potential. Therefore, this research aims to suggest a way to analyze and recognize patents, which enable efficient industry-university cooperation and technology transfer. For the analysis, data on 1,061 patents was collected from 4 different universities. With the data, a quality-strategy matrix was arranged targeting the industry-university cooperation foundations', US patents owned by universities, text mining, and social network analysis were carried out, particularly focusing on the patents in the advanced quality technology section of the matrix. Then core key words and IPC codes were obtained and key patents were analyzed by universities. As a result of the analysis, it was found that 4 key patents, 2 key IPC codes were drawn for University H, 4 key patents, 2 key IPC codes for University K, 6 key patents, 1 key IPC code for University Y, 14 key patents, and 2 key IPC codes for University S. This research is expected to have a great significance in contributing to the invigoration of industry-university cooperation based on the analysis result on patents and IPC codes, which enable efficient industry-university cooperation and technology transfer.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.127-138
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• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Korean Journal of Community Nutrition
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• v.25 no.3
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• pp.189-203
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• 2020

Objectives: This study was performed to identify the current barriers of obesity management for children using Community Child Care Centers and their caregivers (parents and teachers working in the Centers). Further, this study explored the possibility of utilizing a mobile phone application for tailored obesity prevention and management programs to overcome the current difficulties associated with children's obesity management. Methods: The qualitative data were collected through in-depth interviews with 20 obese and overweight children or children who wanted to participate in this study using Community Child Care Centers, 12 teachers working at the Centers, and a focus group interview with five parents of children using the Centers. Data were analyzed with a thematic approach categorizing themes and sub-themes based on the transcripts. Results: The current barriers of obesity management of obese and overweight children using Community Child Care Centers were lack of self-directed motivation regarding obesity management (chronic obesity-induced lifestyles and reduced self-confidence due to stigma) and lack of support from households and Community Child Care Centers (latchkey child, inconsistency in dietary guidance between the Center and household, repetitive pressure to eat, and absence of regular nutrition education). Mobile phone applications may have potential to overcome the current barriers by providing handy and interesting obesity management based on visual media (real-time tracking of lifestyles using behavior records and social support using gamification), environmental support (supplementation of parental care and network-based education between the Community Child Care Center and household), and individualized intervention (encouragement of tailored and gradual changes in eating habits and tailored goal setting). It is predicted that the real-time mobile phone program will provide information for improving nutritional knowledge and behavioral skills as well as lead to sustainable children's coping strategies regarding obesity management. In addition, it is expected that environmental factors may be improved by network-based education between the Community Child Care Centers and households using the characteristics of mobile phones, which are free from space and time constraints. Conclusions: The tailored education program for children using Community Child Care Centers based on mobile phones may prevent and reduce childhood obesity by overcoming the current barriers of obesity management for children, providing environmental and individualized support to promote healthy lifestyles and quality of life in the future.

• Korean Journal of Culture and Social Issue
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• v.22 no.1
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• pp.87-118
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• 2016

The purpose of this present study was to investigate the influence of abusive supervision on strain-based work interference with family and interpersonal deviance. In addition, this study examined the mediating effect of subordinates' emotional labor toward supervisors and the moderating effect of hierarchical organizational climates on emotional labor, perceived organizational family support on strain-based work-family conflict, and social network on interpersonal deviance. The results are summarized as follows: (1) abusive supervision was positively related to subordinates' emotional labor toward supervisors. (2) Emotional labor was positively related to strain-based work-family conflict and interpersonal deviance. (3) Subordinates' emotional labor mediated the relationship between abusive supervision and the two outcome variables. (4) Hierarchical organizational climates moderated the relationship between abusive supervision and emotional labor. (5) Perceived organizational family did not have moderating effect between emotional labor and strain-based work-family conflict. (6) Social network had moderating effect but it did not influence interpersonal deviance as predicted by the hypothesis. Based on the results, implications of findings, limitations, and suggestions for future research were discussed.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.