• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.13-24
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• 2022

In order to explore the context of fraudulent claims and the measures for preventing them targeting the long-term care institutions for the elderly, which is increasing every year in Korea, this study conducted the text mining analysis using the media report articles. The media report articles were collected from the news big data analysis system called 'BIG KINDS' for about 15 years from July 2008 when the Long-Term Care Insurance for the Elderly took effect, to February 28^th 2022. During this period of time, total 2,627 articles were collected under keywords like 'elderly care+fraudulent claims' and 'long-term care+fraudulent claims', and among them, total 946 articles were selected after excluding overlapped articles. In the results of the text mining analysis in this study, first, the top 10 keywords mentioned in the highest frequency in every section(July 1^st 2008-February 28^th 2022) were shown in the order of long-term care institution for the elderly, fraudulent claims, National Health Insurance Service, Long-Term Care Insurance for the Elderly, long-term care benefits(expenses), elderly care facilities, The Ministry of Health & Welfare, the elderly, report, and reward(payment). Second, in the results of the N-gram analysis, they were shown in the order of long-term care benefits(expenses) and fraudulent claims, fraudulent claims and long-care institution for the elderly, falsehood and fraudulent claims, report and reward(payment), and long-term care institution for the elderly and report. Third, the analysis of TF-IDF was similar to the results of the frequency analysis while the rankings of report, reward(payment), and increase moved up. Based on such results of the analysis above, this study presented the future direction for the prevention of fraudulent claims of long-term care institutions for the elderly.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• Knowledge Management Research
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• v.23 no.3
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• pp.129-152
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• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.3
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• pp.371-395
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• 2022

Library and Information Science, traditionally a strong field of book and reading, can be said to play a leading and central role in bibliotherapy that heals human emotions, situations, and pathological symptoms through books and reading processes. Therefore, in this study, the perception and status of bibliotherapy were investigated by applying an academic and practical perspective to the field of library and Information Science, which has a very close relationship with bibliotherapy. First, the perception survey surveyed 200 students taking LIS - related classes at four-year universities in the Seoul metropolitan area on five topics (books, bibliotherapy, curriculum, libraries, and others) using keywords related to bibliotherapy. And then, the current status of bibliotherapy in the field of LIS was investigated by dividing it into a curriculum related to bibliotherapy and bibliotherapy activities in the library to examine it from an educational and practical perspective. Bibliotherapy related curriculum status survey explored and analyzed through bibliotherapy related keywords based on 32 universities with LIS departments among four-year universities in Korea. And bibliotherapy programs and reading lists were analyzed for 1,104 public libraries in Korea. Finally, based on the results of these surveys, the direction for the activation and development of bibliotherapy in the field of LIS was presented from the perspective of bibliotherapy participants and operators.

• 한국체육학회지인문사회과학편
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• v.54 no.3
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• pp.439-452
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• 2015

The purpose of this study is to encourage continuous participation in sports and to provide basic data for the promotion of participation in leisure sports. To achieve the purpose, this study conducted factor scaling analysis on participation motives for leisure sports and subdivided them to analyze psychological reactions of participants. As for study methods, this study collected master and doctor's degree theses and academic journals on motives for sports participation that were conducted from 1997 to 2012 from Korean major search engines. On the search engines, a keyword 'motive' was searched first and then studies on participation motive for leisure sports were collected. Key words that appeared when searching 'motive' were combined with other key words and word spacing between them were checked before conducting a literature analysis. The study results showed that participation motives for leisure sports were divided into a participation motive, an internal motive, an external motive, a leisure motive and other motives. It was identified that there were 23 factors for the participation motive, 17 factors each for the internal motive and the external motive, 8 factors for the leisure motive and 57 factors for other motives. It was found out that 76 factors were used to study a participation motive for leisure sports, excluding the factors that have similar or overlapping meaning based on each factor.

• Journal of Korean Home Economics Education Association
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• v.36 no.2
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• pp.15-31
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• 2024

This study employs photovoice research methodology to investigate adolescents' experiences in sibling relationships and to analyze the impact of sibling interactions on their development and growth. The participants comprised eight high school students with one sibling (four males and four females) residing in the Incheon region. The research process encompassed several stages: participant recruitment, orientation, photovoice activity (focus group interviews), data analysis, and conclusion derivation. During the photovoice activity, participants engaged in a narrative process of photographing, interpreting, and discussing their experiences. The narratives were categorized into four primary themes: structure and environment, emotional interactions, roles, and parental subsystems. The conclusions drawn from the study are as follows: First, the similarity formed in sibling relationships during adolescence contributes to psychological stability. Second, roles and expectations based on birth order can cause stress for adolescents, indicating the need for equitable role adjustments within the family. Third, conflict in sibling relationships is crucial for enhancing problem-solving and social relationship skills. Lastly, consistent parenting attitudes significantly affect the emotional well-being of siblings. This study emphasizes the significance of fostering a deeper understanding of human development and family relationships through an exploration of adolescent sibling dynamics within home economics education.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.1-21
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• 2012

In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 438~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.999-1004
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• 2006

본 연구에서는 다양한 개념 패싯과 관계 패싯들을 수용한 범용 과학기술 시소러스 구축용 웹 기반 워크벤치 개발에 대해 기술한다. 기존 국내 시소러스 구축용 워크벤치들이 제공하는 기본적인 용어 관계구축 기능을 확장하여 개념 패싯, 범주 관계 패싯, 의미역 관계 패싯, 속성 관계 패싯 및 속성 키워드 처리 기능을 원활히 제공할 수 있는 사용자 중심적 워크벤치를 개발함으로써 시소러스 상의 개념들에 대한 효율적인 구축이 가능하도록 한다. 또한 시멘틱 웹 상의 온톨로지 영역에 보다 근접한 고도화되니 시소러스 구축을 위해 용어들을 개념화시키고, 개념간의 다양한 관계를 설정하는 프로세스 중심적 설계로 분야 적합성이 높은 정보 처리 기반을 갖춘다. 궁극적으로 여러 마이크로 시소러스들을 통합하여 운용할 수 있는 복합 모델을 구축하는 것을 목표로 하고 있다. 이러한 목적에 부합하는 시스템 구현을 위해 CBD(Component Based Development) 개발 방법론으로 MSF/CD를 이용하였으며, 분산 환경에서 이기종간의 데이터 교환을 용이하게 하기 위하여 웹 서비스 (XML Web Services)를 이용하였다. 또한 시멘틱 웹 기반 연구자 간 협업 지원 서비스 구현을 위한 확장 검색용으로서도 활용할 수 있도록 하였다. 시소러스 반출은 CSV, XML 및 RDF를 모두 지원할 수 있도록 함으로써 다양한 사용자 요구 사항에 부합할 수 있도록 하였다. 시소러스 브라우징을 시각화 기반의 3단계 구조를 가진 플래시로 구현하여 사용자가 쉽게 시소러스를 탐색하고 분석할 수 있는 기반을 제공하였다. 또한 다양한 검색 요구를 만족시키고자 기본 검색, 고급 검색, 메타 검색을 선택할 수 있도록 하며, 개념 편집 및 시소러스 브라우징과 연동시켜 효율적인 시소러스 구축이 가능하도록 하였다. 본 연구의 워크벤치를 이용하여 구축된 시소러스는 기존 시소러스들에 비해 사용자가 보다 폭넓은 의미 기반 검색을 수행할 수 있도록 함으로써 다각적인 정보를 쉽게 획득할 수 있는 기반을 마련하고 있다는 데 의의가 있으며, 다국어 시소러스 및 다중 시소러스를 수용할 수 있는 방향으로 발전시킬 계획이다.

• Korean Journal of Child Education & Care
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• v.17 no.4
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• pp.163-185
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• 2017

This study set out to investigate the joint homeroom teacher system with two regular teachers at early childhood education institutions in a realistic manner, thus contributing to its application to the field, establishing a system of teachers with professionalism, and providing basic data to create and manage the collaborative capabilities of teachers. For these purposes, the investigator collected and analyzed data from 13 semi-structured individual and group interviews with 16 teachers at K Kindergarten in Gyeonggi Province, eight field observations, and four participant observations for about three months from April to July, 2017. The data were analyzed in the stages of qualitative data analysis involving keyword categories, classification, and discovery of sub-themes. Based on the findings, the study categorized the collaborative management of the joint homeroom teacher system with two regular teachers into "job performance," "difficulties," "institutional supports" and "changes." These findings lead to an expectation that the introduction of the joint homeroom teacher system with two regular teachers will establish a foundation for higher quality of education through the process and changes of collaborative management between two teachers with professionalism.