• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.167-181
• /
• 2023

This study utilized TEXTOM, a social network analysis program to analyze changes in current tourism behavior after travel restrictions were eased after the outbreak of COVID-19. Data on the keywords 'domestic travel' and 'overseas travel' were collected from blogs, cafes, and news provided by Naver, Google, and Daum. The collection period was set from April to December 2022 when social distancing was lifted, and 2019 and 2020 were each set as one year and compared and analyzed with 2022. A total of 80 key words were extracted through text mining and centrality analysis was performed using NetDraw. Finally, through the CONCOR, the correlated keywords were clustered into 4. As a result of the study, tourism behavior in 2022 shows tourism recovery before the outbreak of COVID-19, segmentation of travel based on each person's preferred theme, prioritization of each country's corona mitigation policy, and then selecting a tourist destination. It is expected to provide basic data for the development of tourism marketing strategies and tourism products for the newly emerging tourism ecosystem after COVID-19.

• The Korean Journal of Pain
• /
• v.33 no.2
• /
• pp.153-165
• /
• 2020

Background: Well-validated risk prediction models help to identify individuals at high risk of diseases and suggest preventive measures. A recent systematic review reported lack of validated prediction models for low back pain (LBP). We aimed to develop prediction models to estimate the 8-year risk of developing LBP and its recurrence. Methods: A population based prospective cohort study using data from 435,968 participants in the National Health Insurance Service-National Sample Cohort enrolled from 2002 to 2010. We used Cox proportional hazards models. Results: During median follow-up period of 8.4 years, there were 143,396 (32.9%) first onset LBP cases. The prediction model of first onset consisted of age, sex, income grade, alcohol consumption, physical exercise, body mass index (BMI), total cholesterol, blood pressure, and medical history of diseases. The model of 5-year recurrence risk was comprised of age, sex, income grade, BMI, length of prescription, and medical history of diseases. The Harrell's C-statistic was 0.812 (95% confidence interval [CI], 0.804-0.820) and 0.916 (95% CI, 0.907-0.924) in validation cohorts of LBP onset and recurrence models, respectively. Age, disc degeneration, and sex conferred the highest risk points for onset, whereas age, spondylolisthesis, and disc degeneration conferred the highest risk for recurrence. Conclusions: LBP risk prediction models and simplified risk scores have been developed and validated using data from general medical practice. This study also offers an opportunity for external validation and updating of the models by incorporating other risk predictors in other settings, especially in this era of precision medicine.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.5
• /
• pp.279-284
• /
• 2017

Currenty, in the era of big data, text mining and opinion mining have been used in many domains, and one of their most important research issues is to extract significant information from social media. Thus in this paper, we propose a logistic regression ensemble method of finding the main body text from blog HTML. First, we extract structural features and text features from blog HTML tags. Then we construct a classification model with logistic regression and ensemble that can decide whether any given tags involve main body text or not. One of our important findings is that the main body text can be found through 'depth' features extracted from HTML tags. In our experiment using diverse topics of blog data collected from the web, our tag classification model achieved 99% in terms of accuracy, and it recalled 80.5% of documents that have tags involving the main body text.

• Journal of Industrial Convergence
• /
• v.20 no.7
• /
• pp.65-71
• /
• 2022

With the development of ICT, as the era of the 4th industrial revolution arrives, the amount of data is enormous, and as big data technologies emerge, technologies for processing, storing, and processing data are becoming important. In this paper, we propose a system that detects events through monitoring and judges them using hash values because the damage to important files in case of leakage in industries and public places is serious nationally and property. As a research method, an optional event method is used to compare the hash value registered in advance after performing the encryption operation in the event of a file leakage, and then determine whether it is an important file. Monitoring of specific events minimizes system load, analyzes the signature, and determines it to improve accuracy. Confidentiality is improved by comparing and determining hash values pre-registered in the database. For future research, research on security solutions to prevent file leakage through networks and various paths is needed.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.34 no.4
• /
• pp.259-283
• /
• 2023

This study acknowledges that prevailing regulation concerning for the long-term preservation of electronic records focus mainly on document types, neglecting the preservation of electronic records from various administrative information systems. With the growing interest in data management in the era of big data, it is imperative to establish clear standards for the long-term preservation of datasets. The choice of preservation format for electronic records is based on the specific standards for each type of electronic record. These standards are formulated according to the significant properties relevant to the electronic record type. This study aims to identify the significant properties of electronic records of each record type, before creating specific preservation format selection criteria for these record types. To achieve this, we reviewed and analyzed R&D studies by the National Archives of Korea and the NARA in the United States. As a result of the research, 9 significant properties were identified for database-type entities, and 7 significant properties were identified for structured data-type entities.

• Journal of Korean Home Economics Education Association
• /
• v.31 no.4
• /
• pp.149-161
• /
• 2019

At present, we are at the point of change of the 4th industrial revolution era due to the development of artificial intelligence(AI) and rapid technological innovation that no one can predict until now. This study started from the question of 'What role should home economics education play in the era of the Fourth Industrial Revolution?'. The Fourth Industrial Revolution is characterized by AI, cloud computing, Internet of Things(IoT), big data, and Online to Offline(O2O). It will drastically change the social system, science and technology and the structure of the profession. Since the dehumanization of robots and artificial intelligence may occur, the 4th Industrial Revolution Education should be sought to foster future human resources with humanity and citizenship for the future community. In addition, the implication of education in the fourth industrial revolution, which will bring about a change to a super-intelligent and hyper-connected society, is that the role of education should be emphasized so that humans internalize their values as human beings. Character education should be established as a generalized and internalized consciousness with a concept established in the integration of the curriculum, and concrete practical strategies should be prepared. In conclusion, home economics education in the 4th industrial revolution era should play a leading role in the central role of character education, and intrinsic improvement of various human lives. The fourth industrial revolution will change not only what we do, or human mental and physical activities, but also who we are, or human identity. In the information society and digital society, it is important how quickly and accurately it is possible to acquire scattered knowledge. In the information society, it is required to learn how to use knowledge for human beings in rapid change. As such, the fourth industrial revolution seeks to lead the family, organization, and community positively by influencing the systems that shape our lives. Home economics education should take the lead in this role.

• Journal of Digital Convergence
• /
• v.17 no.10
• /
• pp.49-58
• /
• 2019

In the era of the 4th industrial revolution, the development of information technology brought various benefits, but it also increased social interest in privacy issues. As the possibility of personal privacy violation by big data increases, academic discussion about privacy management has begun to be active. While the traditional view of privacy has been defined at various levels as the basic human rights, most of the recent research trends are mainly concerned only with the information privacy of online privacy protection. This limited discussion can distort the theoretical concept and the actual perception, making the academic and social consensus of the concept of privacy more difficult. In this study, we analyze the privacy concept that is exposed on the internet based on 12,000 news data of the portal site for the past one year and compare the difference between the theoretical concept and the socially accepted concept. This empirical approach is expected to provide an understanding of the changing concept of privacy and a research direction for the conceptualization of privacy for current situations.

• Journal of the Society of Disaster Information
• /
• v.17 no.3
• /
• pp.589-599
• /
• 2021

Purpose: A reliable indicator of congested traffic speed is essential in providing the information of traffic flow states about motorway sections. The aim of this study is to propose an adaptive indicator of congested speed which is employed for deciding the traffic flow states for individual motorway sections using disaggregated section-based speed data. Method: Typically, the state of traffic flow is categorized into the three: uncongested, mixed, congested states. A method, presented in this study, was developed for identifying boundary speed values of road sections through categorizing the three traffic flow states with individual vehicular speed values. The boundary speed state of each road segment is determined using the speed distributions of mixed and congested traffic states. Result: Analysis results revealed that boundary speed values between mixed and congested states for road sections were similar to those of US and EU criteria (i.e., 48.28~66.0 kph). This indicates that boundary speed values could be different according to road sections. Conclusion: It is expected that the method and indicator, proposed in this study, could be efficaciously used for providing ad-hoc real-time traffic states and computing traffic congestion costs for motorway sections in the era of big data.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.2
• /
• pp.492-497
• /
• 2019

This study analysed the legal concept of personal information(PI), which was not differentiated from behavioral information, and established it clearly for invigorating online targeted advertising(OTA), which draw attention in big data era; by selecting Guidelines of Assessment of Data Breach Incident Factors and Guidelines of Measures for No-Identifying Personal Information based on Personal Information Protection Act(PIPA) and Enforcement Decree of the PIPA. As a result, PI was defined as any kind of information relating to (1)a living individual(not group, corporate body or things etc.); (2)makes possibly identify the individual by his or her identifiers such as name, resident registration number, image, etc. (not included if not identify the individual); and (3)including information like attribute values which makes possibly identify any specific individual, if not by itself, but combined with other information which can be actually collected and combined). Specifically, PI includes basic, proper distinguishable, sensitive and other PI. It is suggested that PI concept should be researched continually with digital technology development; the effectiveness of the Guidelines of PI Protection in OTA, the legal principles of PI protection from not only users' but business operators' perspectives and the differentiation between PI and behavioral information in OTA should be researched.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.5
• /
• pp.317-324
• /
• 2021

This study deals with keywords from social media on domestic portal sites related to COVID-19, which is spreading widely. The data were collected between January 20 and August 15, 2020, and were divided into three stages. The precursor period is before COVID-19 started spreading widely between January 20 and February 17, the serious period denotes the spread in Daegu between February 18 and April 20, and the stable period is the decrease in numbers of confirmed infections up to August 15. The top 50 words were extracted and clustered based on TF-IDF. As a result of the analysis, the precursor period keywords corresponded to congestion of the Situation. The frequent keywords in the serious period were Nation and Infection Route, along with instability surrounding the Treatment of COVID-19. The most common keywords in all periods were infection, mask, person, occurrence, confirmation, and information. People's emotions are becoming more positive as time goes by. Cafes and blogs share text containing writers' thoughts and subjectivity via the internet, so they are the main information-sharing spaces in the non-face-to-face era caused by COVID-19. However, since selectivity and randomness in information delivery exists, a critical view of the information produced on social media is necessary.