• Journal of Multimedia Information System
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• v.6 no.4
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• pp.217-224
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• 2019

As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.492-501
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• 2019

To use the system safely in cyberspace, you need to use a security solution that is appropriate for your situation. In order to strengthen cyber security, it is necessary to accurately understand the flow of security from past to present and to prepare for various future threats. In this study, information security words of security/hacking news of Naver News which is reliable by using text mining were collected and analyzed. First, we checked the number of security news articles for the past seven years and analyzed the trends. Second, after confirming the security/hacking word rankings, we identified major concerns each year. Third, we analyzed the word of each security solution to see which security group is interested. Fourth, after separating the title and the body of the security news, security related words were extracted and analyzed. The fifth confirms trends and trends by detailed security solutions. Lastly, annual revenue and security word frequencies were analyzed. Through this big data news analysis, we will conduct an overall awareness survey on security solutions and analyze many unstructured data to analyze current market trends and provide information that can predict the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.111-119
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• 2023

This study aimed to analyze the changes in news articles related to 'Elderly Living Alone' by comparing Big Data-based news articles related to 'Elderly Living Alone' reported before and after the outbreak of COVID-19. For this, 2018 to 2019 were selected before the outbreak of COVID-19, and 2020 to 2021 were selected after the outbreak, and news articles related to 'Elderly Living Alone' were collected and analyzed using BIGKinds. The main results are as follows. First, the number of related articles decreased after the outbreak of COVID-19 compared to before. Second, there was no significant difference in the analysis of related words. Third, in the relationship diagram analysis, 'Executives' before the outbreak of COVID-19 and 'Corona 19' after that showed the most weight. This study is expected to be used as basic data in preparing improvement plans for national policies and systems in the context of the spread of infectious diseases in relation to 'Elderly Living Alone'.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.107-132
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• 2024

This study attempted to analyze newspaper articles related to May 18 through frequency analysis and network analysis using news data related to May 18 for about 30 years from 1990 to 2022 at the Korea Press Foundation's Big Kinds. Specifically, quantitative change trends were examined by analyzing the amount of articles by period and region, and the connection structure between major keywords by the regime was explored through network analysis by regime using co-appearance keywords. As a result of the analysis, it was found that 2019 had the largest amount of coverage, which had many social issues in time, and the Jeolla-do region had the largest amount of coverage in the region. And as a result of network analysis, there were differences in words related to May 18 in news data according to the perception and policy of the regime toward May 18. As a result of synthesizing the analysis of May 18 news data, it was confirmed that May 18 was becoming a democratic movement over time regardless of region, but at the same time, the distortion of May 18 was not resolved.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.2
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• pp.85-101
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• 2021

This study aims to gain insight through understanding the Metaverse, which has recently become a hot topic. The study utilizes the methods of case study and News Bigdata Analysis Services. The Metaverse can be defined as a world with no separation between the virtual and real worlds. Currently, the Metaverse is dominated mainly by the MZ generation, but just like smartphones have quickly entered our lives, the Metaverse will soon, too, become a part of our lives. To follow up on this change, all companies, including global companies, are going after the Metaverse. Today, the Metaverse is successfully being used in all types of fields, including gaming, performing arts, business, etc., and its essential technologies include VR/AR/MR/XR and AI. This study intends to help understand the Metaverse through a case analysis of Zepeto, which has 200 million users worldwide. On Zepeto, users can decorate their own avatars, hang out with friends, go to art galleries and performances, and create and sell items. Of these users, 90% are from outside of South Korea, and 80% are teenagers. With most of the users being underage, many legal and social problems also follow. Nevertheless, who will be the first to conquer the new world of the Metaverse will continue to be a big issue. This study also analyzes domestic news articles about the Metaverse by utilizing the BigKinds system. Starting in 1996, the number of articles about the Metaverse each year remains single digit, until in 2020 when the number sharply rises to 86 news. As of June 2021, there are 1,663 articles on the Metaverse. This study suggests that the Metaverse should now be carefully examined and closely followed.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.678-679
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• 2015

Recently, safety accidents in construction sites are increasing. Accordingly, in this study, development of 'Big-Data Analysis Modeling' can collect articles from last 10 years which came from the Internet News and draw the cause of accidents that happening per season. In order to apply this study, Web Crawling Modeling that can collect 98% of desired information from the internet by using 'Xml', 'tm', "Rcurl' from the library of R, a statistical analysis program has been developed, and Datamining Model, which can draw useful information by using 'Principal Component Analysis' on the result of Work Frequency of 'Textmining.' Through Web Crawling Modeling, 7,384 out of 7,534 Internet News articles that have been posted from the past 10 years regarding "safety Accidents in construction sites", and recognized the characteristics of safety accidents that happening per season. The result showed that accidents caused by abnormal temperature and localized heavy rain, occurred frequently in spring and winter, and accidents caused by violation of safety regulations and breakdown of structures occurred frequently in spring and fall. Plus, the fact that accidents happening from collision of heavy equipment happens constantly every season was acknowledgeable. The result, which has been obtained from "Big-Data Analysis Modeling" corresponds with prior studies. Thus, the study is reliable and able to be applied to not only construction sites but also in the overall industry.

• Journal of Information Technology Services
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• v.11 no.3
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• pp.39-48
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• 2012

It is essential that firm makes a rational and scientific decision making and creates a news value for the future direction. To do so, many firms attempt to collect meaningful data and find the filtered and refined implication for the better customer relationship and the active market drive through the various analytic tools. Among the possible IT solutions, utilization of 'Big Data' is becoming more attractive and necessary in such a way that it would help firms obtain the systemized and demanding information and facilitate their decision making process to keep up with the market needs. In this paper, it introduces the concepts and development of 'Big Data' recognized as a IT resource and solution under the rapidly changing firm environment. This study also presents the several firm cases using Big Data' and the Oracle's total data management and analytic solutions in order to support the application of 'Big Data'. Finally this paper provides a holistic viewpoint and realistic approach on use of 'Big Data' to create a new value.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.551-560
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• 2020

Due to the nature of fashion design that responds quickly and sensitively to changes, accurate forecasting for upcoming fashion trends is an important factor in the performance of fashion product planning. This study analyzed the major phenomena of fashion trends by introducing text mining and a big data analysis method. The research questions were as follows. What is the key term of the 2010SS~2019FW fashion trend? What are the terms that are highly relevant to the key trend term by year? Which terms relevant to the key trend term has shown high frequency in news articles during the same period? Data were collected through the 2010SS~2019FW Pre-Trend data from the leading trend information company in Korea and 45,038 articles searched by "fashion+material" from the News Big Data System. Frequency, correlation coefficient, coefficient of variation and mapping were performed using R-3.5.1. Results showed that the fashion trend information were reflected in the consumer market. The term with the highest frequency in 2010SS~2019FW fashion trend information was material. In trend information, the terms most relevant to material were comfort, compact, look, casual, blend, functional, cotton, processing, metal and functional by year. In the news article, functional, comfort, sports, leather, casual, eco-friendly, classic, padding, culture, and high-quality showed the high frequency. Functional was the only fashion material term derived every year for 10 years. This study helps expand the scope and methods of fashion design research as well as improves the information analysis and forecasting capabilities of the fashion industry.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.154-161
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• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.41-46
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• 2022

The purpose of this study is to confirm and analyze the impact on consumers through big data keyword analysis on weak food. For data collection, web documents, blogs, news, cafes, intellectuals, academic information, and Google Web, news, and Facebook provided by Naver and Daum were used as analysis targets. The data analysis period was set from January 2018 to December 2021. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analyzed and visualized using the Netdraw function among UCINET6 programs. In addition, CONCOR analysis was conducted to derive clusters for similar keywords. As a result of analyzing yakseon food with keywords, a total of 35,985 cases of collected data were derived. Through this, it was confirmed that medicinal food affects consumers. Furthermore, if a business model is created and developed through yakseon food, it will be possible to lead the popularization of yakseon food.