• The Korean Journal of Food & Health Convergence
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• v.10 no.1
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• pp.1-6
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• 2024

Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.241-248
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• 2014

It has been expanded the bandwidth of data usages due to the rapid developments of information technology and infra hardware and then it was proposed to new paradigm of Big Data era. It has a trend to increase a Big Data technology and its performance gradually, thus enterprises have realized the importance of Data and the movement to take advantage of Big Data becomes active. This study has been performed to verify the importance through select the factors in order to active adoption of Big Data technology and utilization when enterprises use Big Data. It was selected that Big Data characteristic factors are the natures of predictability, manageability, affordability, competitiveness, creativity, responsiveness and supportability on the study. It is verified and showed that manageability were influenced to introduce Big Data in order, at the result of survey and statistics for enterprise practitioners who have big data experience.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.49-55
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• 2019

This study is on the trend of real estate market using text mining and big data. The data were collected through internet news posted on Naver from August 2016 to August 2017. As a result of TF-IDF analysis, the frequency was high in the order of housing, sale, household, real estate market, and region. Many words related to policies such as loan, government, countermeasures, and regulations were extracted, and the region - related words appeared the most frequently in Seoul. The combination of the words related to the region showed that the frequencies of 'Seoul - Gangnam', 'Seoul - Metropolitan area', 'Gangnam - reconstruction' and 'Seoul - reconstruction' appeared frequently. It can be seen that the people's interest and expectation about the reconstruction of Gangnam area is high.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.177-185
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• 2020

Recently, the popularity of professional baseball is increasing day by day, and it has data related to professional baseball on various portal sites. If you want to increase the popularity of professional baseball and produce results through analysis using relevant data, you have the advantage of accessing professional baseball. In this paper, three analyzes were conducted using data related to professional baseball. Therefore, in this paper, the trend related to the number of articles retrieved from a specific site of a professional baseball team was examined, and the correlation between professional baseball scores and the number of spectators was analyzed. Finally, we analyzed the current status of professional baseball batting average and on base percentage in 2016 and 2017.

• Journal of the Korea Fashion and Costume Design Association
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• v.22 no.3
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• Asia pacific journal of information systems
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• v.29 no.3
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• pp.414-427
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• 2019

The sharing economy has significantly changed the way of living for years. The emergence and expansion of sharing economy empowered by the mobile information technologies and intellectual algorithms reconfigure how people use transportation means. In this paper, the bike sharing phenomenon is highlighted. Combining a big data set provided by the Seoul government about user logs and air quality data set, the empirical findings reveal that temperature change is tightly associated bike sharing activities. Also, the concentration of particulate matter is weakly related to bike sharing, but the trend should be carefully examined. By considering external environmental factors to bike sharing businesses, this work is differentiated. To further understand empirical data, data mining methods and econometric approaches were adopted.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.108-113
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• 2024

The purpose of this study is to analyze user perceptions of tourism platforms through big data. Data were collected from Naver, Daum, and Google as big data analysis channels. Using semantic network analysis with the keyword 'tourism platform,' a total of 29,265 words were collected. The collection period was set for two years, from August 31, 2021, to August 31, 2023. Keywords were analyzed for connected networks using TexTom and Ucinet programs for social network analysis. Keywords perceived by tourism platform users include 'travel,' 'diverse,' 'online,' 'service,' 'tourists,' 'reservation,' 'provision,' and 'region.' CONCOR analysis revealed four groups: 'platform information,' 'tourism information and products,' 'activation strategies for tourism platforms,' and 'tourism destination market.' This study aims to expand and activate services that meet the needs and preferences of users in the tourism field, as well as platforms tailored to the changing market, based on user perception, current status, and trend data on tourism platforms.

• The Journal of Bigdata
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• v.3 no.1
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• pp.63-71
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• 2018

In the last five years (2013~2017), we analyzed what factors influenced Korean films that have surpassed 10 million viewers in the Korean movie industry, where the total number of moviegoers is over 200 million. In general, many people consider the number of screens and ratings as important factors that affect the audience's success. In this study, four additional factors, including the number of screens and ratings, were established to establish a hypothesis and correlate it with the presence of 10 million spectators through big data analysis. The results were significant, with 91 percent accuracy in predicting 10 million viewers and 99.4 percent accuracy in estimating cumulative attendance.

• Journal of Platform Technology
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• v.11 no.4
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• pp.19-34
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• 2023

In order to identify the trends and insights of 'startups' in the global era, we conducted an in-depth trend analysis of the global startup ecosystem using Google Trends, a big data analysis platform. For the validity of the analysis, we verified the correlation between the keywords 'startup' and 'global' through BIGKinds. We also conducted a network analysis based on the data extracted using Google Trends to determine the frequency of searches for the keyword or term 'startup'. The results showed a strong positive linear relationship between the keywords, indicating a statistically significant correlation (correlation coefficient: +0.8906). When exploring global startup trends using Google Trends, we found a terribly similar linear pattern of increasing and decreasing interest in each country over time, as shown in Figure 4. In particular, startup interest was low in the range of 35 to 76 from mid-2020 due to the COVID-19 pandemic, but there was a noticeable upward trend in startup interest after March 2022. In addition, we found that the interest in startups in each country except South Korea is very similar, and the related topics are startup company, technology, investment, funding, and keyword search terms such as best startup, tech, business, invest, health, and fintech are highly correlated.

• Journal of Smart Tourism
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• v.1 no.2
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• pp.3-12
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• 2021

The interaction between technology and tourism has been a dynamic research area recently. This study aims to review the progress and development of technology in tourism research via a bibliometric analysis. We derive the source data from the Web of Science (WoS) core collection and use CiteSpace for bibliometric analysis, including countries, institutions, authors, categories, references, and keywords. The analysis results are as follows: i) The number of published articles on the role of technology in tourism has increased in recent years. ii) Technology-related articles in tourism are abundant in Tourism Management, Journal of Travel Research, and Annals of Tourism Research. iii) The countries with the most contributions are China, the US, and the UK. The most active institutions are the Hong Kong Polytechnic University, University of Central Florida, Bournemouth University, University of Queensland, and Kyung Hee University. iv) The reference analysis results identify eight extensively researched topics from the most cited papers, and the keyword burst analysis results present an emerging trend. This study identifies the effect and development of technology in tourism research. Our findings provide implications for researchers about the current research focus of technology and the future research trend of technology in the tourism field.