• The Journal of Industrial Distribution & Business
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• v.13 no.9
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• pp.27-36
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• 2022

Purpose: The coronavirus pandemic has affected the tourism industry in a big way. The travel industry suffered intense damage from the pandemic and procedures acquainted to containing its spread because the pandemic outbreak has led to a decline in the number of tourists and a change in their behavior. At this point, this research is to investigate adequate solutions for tourism industry to overcome the crisis in a post-Covid 19 era. Research design, data and methodology: The current author gathered data from each included study to analyze and summarize the evidence when conducting a literature analysis. This stage involves gathering and reviewing intricate texts databases for the meta-analysis. Results: The current author found total five solutions from numerous literature contents, suggesting how to overcome the crisis in a post-Covid era for tourism industry. Solutions as follows, (1) Drawing beginning illustrations, (2) Introducing Government Backing Programs, (3) Increasing Promotion of Tourism Destinations, (4) Enhancing Safety and Security Measures, and (5) Improving Infrastructure and Facilities. Conclusions: This research suggests that although the global economic recession leads to reduced demand and intense competition from other sectors, the tourism industry will be well positioned to weather these challenges if practitioners of tourism organizations follow five solutions of this research.

• International Journal of Contents
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• v.14 no.3
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• pp.27-31
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• 2018

Social network service (SNS) data related to green tourism were used to estimate preferred tour sites and users' interests. Keywords related with green tour activities were employed to search the SNS data. SNS data were collected from Korean blogs such as Naver and Daum from June $1^{st}$ to August $31^{st}$ between 2015 and 2017 using text-mining solution. During the study period, seven hundred and five posts were analyzed. Associated words that frequently co-occurred with keywords were classified into different categories depending on the nature of associated words. Associated words included swimming pools and camping sites (location); experience and swimming pools (attribute); and water play and culture (culture/leisure). Our data suggest that SNS users with experience of green tourism in Korea exhibited interest in green tourism with swimming pools, camping sites, experience, water play and/or culture rather than particular popular sites. Based on the findings, it is recommended that preferred facilities such as swimming pools should be provided at green tourism sites to meet the users' needs and to facilitate green tourism.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.4
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• pp.299-307
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• 2024

Fishing villages, which are the focus of this study, are interested in fishing tourism for creating a new income base and sustaining fishing communities. This is because the extraordinary nature of the fishing village space creates new values in line with the function of tourism, however, the related policies are less than adequate compared to the importance of fishing village tourism. Therefore, this study aims to analyze the interest of Korean society in fishing village tourism the manner in which this issue has changed over time. Using the news analysis system, BigKinds, we systematically collected and analyzed articles related to fishing village tourism reported in the domestic media. The results showed that social interest in fishing village tourism and government policy support had increased over time, suggesting that fishing village tourism was an important strategy that could revitalize local economies and prevent the disappearance of fishing villages.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.671-672
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• 2014

최근 기업의 효율적인 경영을 위해 다양한 소셜 채널에서 폭발적으로 생성되고 확산되는 빅 데이터를 실시간으로 분석하는 기술이 개발되고 있다. 본 논문에서는 관광서비스에 관해 소셜 미디어 상의 빅 데이터를 이용하여 보다 정확하고 효율적인 정보 수집과 분석이 가능하도록 하기위한 모델구축 방법을 제안하고 관광서비스에 관한 평판을 분석한다. 관광 산업 도메인 네트워크를 활용한 표준화, 일반화 확보를 위해 먼저 B2C 산업군 및 업종별 공통 수집원 추출 및 표준화 분석 체계 수립을 통한 해당 적용분야의 설계안 수립하고 관광객(소비자) 작성 게시글 분석을 위한 산업군 정보 추출하며 관광지, 숙박지, 교통 등 다양한 업종에 대한 분석 수행한다. 관광지에 대한 평가 기준을 기존의 설문이 아닌 SNS 상의 고객 의견을 바탕으로 호감도로 분석한다.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.179-184
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• 2024

Franchise are now a red ocean in Food industry and they need to find other options to appeal for their product, the uprising content, food tech. The franchises are working on R&D to help franchisees with the operations. Through this paper, we analyze the franchise interest on food tech and to help find the necessity of development for franchisees who are in needs with hand, not of human, but of technology. Using Textom, a big data analysis tool, "franchise" and "food tech" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 01 January, 2023 to 31 December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "food tech" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, a total of 10,049 words were derived, and among them, the top 50 keywords with the highest relevance and search frequency were selected and applied to this study. The top 50 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. By using big data analysis, it was found out that franchise do have interest on food tech. "technology", "franchise", "robots" showed many interests and keyword "R&D" showed that franchise are keen on developing food tech to seize competitiveness in Franchise Industry.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.353-360
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• 2023

This paper explores notable shifts in the restaurant startup market following the lifting of social distancing measures. Key trends identified include an escalated interest in startups, a heightened focus on the quality and diversity of food, a relative decline in the importance of delivery services, and a growing interest in specific industry sectors. The study's data collection spanned three years, from April 2021 to May 2023, encompassing the period before and after social distancing. Data were sourced from a range of online platforms, including blogs, news sites, cafes, web documents, and intellectual forums, provided by Naver, Daum, and Google. From this collected data, the top 50 words were identified through a refinement process. The analysis was structured around the social distancing application period, comparing data from April 2021 to April 2022 with data from May 2022 to May 2023. These observed trend changes provide founders with valuable insights to seize new market opportunities and formulate effective startup strategies. In summary, We offer crucial insights for founders, enabling them to comprehend the evolving dynamics in food service startups and to adapt their strategies to the current market environment.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.332-341
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• 2023

Notable trends in the restaurant start-up market after the lifting of social distancing include increasing interest in start-ups, emphasizing the importance of food quality and diversity, decreasing the relative importance of delivery services, and increasing interest in certain industries. The data collection period is three years from April 2021 to May 2023, including before and after social distancing, and texts extracted from blogs, news, cafes, web documents, and intellectuals provided by Naver, Daum, and Google were collected. For the collected data, the top 30 words were derived through a refining process. In addition, based on April 2021, the application period of social distancing, data from April 2021 to April 2022, and data from May 2022 to May 2023, Through these changes in trends, founders can capture new opportunities in the market and develop start-up strategies. In conclusion, this paper provides important insights for founders in accurately understanding the changes in food service start-up trends and in developing strategies appropriate to the current market situation.

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

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.174-180
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• 2021

The necessity of this study is as follows. A decrease in the number of newborns, an increase in the youth unemployment rate, and a decrease in the employment rate are having a fatal impact on universities. To help increase the employment rate of universities, we intend to utilize Big Data of university public information. Big data refers to the process of collecting and analyzing data, and includes all business processes of finding data, reprocessing information in an easy-to-understand manner, and selling information to people and institutions. Big data technology can be divided into technologies for storing, refining, analyzing, and predicting big data. The purpose of this study is to find the vision and special department of a university with a high employment rate by using big data technology. As a result of the study, big data was collected from 227 universities on www.academyinfo.go.kr site, We selected 130 meaningful universities and selected 25 universities with high employment rates and 25 universities with low employment rates. In conclusion, the university with a high employment rate can first be said to have a student-centered vision and university specialization. The reason is that, for universities with a high employment rate, the vision was to foster talents and specialize, whereas for universities with a low employment rate, regional bases took precedence. Second, universities with a high employment rate have a high interest in specialized departments. This is because, as a result of checking the presence or absence of a characterization plan, universities with a high employment rate were twice as high (21/7). Third, universities with high employment rates promote social needs and characterization. This is because the characteristic departments of universities with high employment rates are in the order of future technology and nursing and health, while universities with low employment rates promoted school-centered specialization in future technology and culture, tourism and art. In summary, universities with high employment rates showed high interest in student-centered vision and development of special departments for social needs.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.37-50
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• 2021

Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.