• Convergence Security Journal
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• v.22 no.1
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• pp.55-60
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• 2022

This study is to analyze trends and relational structures in the energy sector related to renewable energy. For this reason, in this study, we focused on big data including SNS data. SNS utilizes the Instagram platform to collect renewable energy hash tags and use them as a word embedding method for big data analysis and social network analysis, and based on the results derived from this research, it will be used for the development of the renewable energy industry. It is expected that it can be utilized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.1
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• pp.193-199
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• 2016

Recently there are many researches about analyzing the interaction between entities by social network analysis in various fields. In this paper, we are going to analyze the author's interests area at the biography section in Journal of the Korea Institute of Information and Communication Engineering by social network analysis. The results show that many authors in that journal are mainly focusing on embedded, security, image processing, wireless network, big data, USN, network, RFID.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.861-863
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• 2016

대부분의 기업은 경영전략을 결정하는데 고객의 소리(VOC:Voice of Customer)를 매우 중요한 정보로 사용하고 있기 때문에 기업들은 다양한 방법으로 고객과의 관계증진을 위해 VOC 데이터를 이용하고 있다. 그러나 수집된 내부VOC 데이터에서 많은 정성적인 데이터를 포함하고 있으므로 분석하는 데는 한계가 있다. 본 논문에서는 최근 소셜 빅 데이터를 분석하는데 사용하고 있는 시스템을 이용하여 다른 업종에 비해 고객이 다양하고 서비스가 매우 중요한 여행사 내부 VOC를 분석한다. 적용 사례로서 국내 대표적인 여행사에 직접 적용하여 분석한 결과를 제시한다. 본 연구 결과 빅 데이터 분석 도구를 다른 서비스업종의 내부 VOC의 정성적인 데이터를 분석하는데 활용할 수 있는 가능성을 보여주었다고 사료된다.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.269-281
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• 2016

The process of spatial big data, such as social media, is being paid more attention in the field of spatial information in recent years. This study, as an example of spatial big data analysis, analyzed the spatial and temporal distribution of Tweet data based on the location and time information. In addition, the characteristics of its spatial pattern by times were identified. Tweet data in Busan city are collected, processed, and analyzed to identify the characteristics of the temporal and spatial pattern. Then, the results of Tweet data analysis were compared with the characteristics of the land type. This study found that spatial pattern of tweeting in the city was associated with given time periods such as daytime and nighttime in both weekdays and weekends. The spatial distribution patterns of individual time periods were compared with the characteristics of the land for the spatially concentrated area. The results of this study showed that tweeted data would be related to different spatial distribution depending on the time, which potentially reflects the daily pattern and characteristics of the land type of urban area to some extent. This study presented the possible incorporation of social media data, e. g. Tweet data, into the field of spatial information. It is expected that there will be more advantage to use a variety of social media data in areas such as land planning and urban planning.

• 한국체육학회지인문사회과학편
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• v.56 no.2
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• pp.303-320
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• 2017

This paper seeks to better understand the focal issues and persons related to Rio Olympic soccer games through social data science and analytics. This study collected its data from online news articles and comments specific to KOR during the Olympic football games. In order to investigate the public interests for each game and target persons, this study performed the co-occurrence words analysis. Then after, the study applied the NodeXL software to perform its visualization of the results. Through this application and process, the study found several major issues during the Rio Olympic men's football game including the following: the match between KOR and PIJ, KOR player Heungmin Son, commentator Young-Pyo Lee, sportscaster Woo-Jong Jo. The study also showed the general public opinion expressed positive words towards the South Korean national football team during the Rio Olympics, though there existed negative words as well. Furthermore the study revealed positive attitude towards the commentators and casters. In conclusion, the way to increase the public's interest in big sporting events can be achieved by providing the following: contents that include various professional sports analysis, a capable domain expert with thorough preparation, a commentator and/or caster with artistic sense as well as well-spoken, explanatory power and so on. Multidisciplinary research combined with sports science, social science, information technology and media can contribute to a wide range of theoretical studies and practical developments within the sports industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.23 no.3
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• pp.201-209
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• 2017

The Big Data, the element of the Fourth Industrial Revolution, is drawing attention as the 4th Industrial Revolution is mentioned in the 2016 World Economic Forum. Big Data is being used in various fields because it predicts the near future and can create new business. However, utilization and research in the field of packaging are lacking. Today packaging has been demanded marketing elements that effect on consumer choice. Big data is actively used in marketing. In the marketing field, big data can be used to analyze sales information and consumer reactions to produce meaningful results. Therefore, this study proposed a method of applying big data in the field of packaging focusing on marketing. In this study suggest that try to utilize the private data and community data to analyze interaction between consumers and products. Using social big data will enable to understand the preferred packaging and consumer perceptions and emotions in the same product line. It can also be used to analyze the effects of packaging among various components of the product. Packaging is one of the many components of the product. Therefore, it is not easy to understand the impact of a single packaging element. However, this study presents the possibility of using Big Data to analyze the perceptions and feelings of consumers about packaging.

• Korea Information Processing Society Review
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• v.21 no.3
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• pp.26-34
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• 2014

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.87-94
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• 2019

In this paper, the method of analyzing the unformatted data of consumers accumulated on social networks in the era of the Fourth Industrial Revolution by utilizing data from the service design and social psychology aspects was proposed. First, the fandom phenomenon, which shows subjective and collective behavior in a space on a social network rather than physical space, was defined from a data service perspective. The fandom model has been transformed into a collective level of customer Persona that has been analyzed at a personal level in traditional service design, and social network analysis that analyzes consumers' big data has been presented as an efficient way to pattern and visually analyze it. Consumer data collected through social leasing were pre-processed by column based on correlation, stability, missing, and ID-ness. Based on the above data, the company's brand strategy was divided into active and passive interventions and the effect of this strategic attitude on the growth direction of the consumer's fandom community was analyzed. To this end, the fandom model of consumers was proposed by dividing it into four strategies that the brand strategy had: stand-alone, decentralized, integrated and centralized, and the fandom shape of consumers was proposed as a growth model analysis technique that analyzes changes over time.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.103-112
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• 2014

In recent years, the rapid diffusion of smart devices and growth of internet usage and social media has led to a constant production of huge amount of valuable data set that includes personal information, buying patterns, location information and other things. IT and Production Infrastructure has also started to produce its own data with the vitalization of M2M (Machine-to-Machine) and IoT (Internet of Things). This analysis study researches the applicable effects of Structured and Unstructured Big Data in various business circumstances, and purposes to find out the value creation method for a corporation through the Structured and Unstructured Big Data case studies. The result demonstrates that corporations looking for the optimized big data utilization plan could maximize their creative values by utilizing Unstructured and Structured Big Data generated interior and exterior of corporations.