• Journal of Digital Convergence
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• v.18 no.2
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• pp.367-376
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• 2020

According to recent statistics on new consumer market trends, 'alone consumption' is at the center. This study focuses on the social big data that attracts the public's opinions in that it is important for a certain social trend to comprehensively understand the various fields such as society, locality, culture, marketing, economics, and psychology that form the background for it. Therefore, we set up the linkage of 'solo consumption' and conducted research on new consumer market trends using Opinion Analisys. As a result of this trend analysis, representative keywords such as 'honbab', 'honsul' and 'honyoeng' were derived and analyzed the trend of new consumer market using this data. Alone consumption is an inevitable new consumption trend caused by demographic change after the global economic crisis. The importance as a trend reflecting this will be further strengthened. Trend analysis by social big data will help scientific and systematic business distribution strategies and planning to help make new and valuable decisions and decisions about new consumer markets.

• Journal of Digital Convergence
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• v.12 no.9
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• pp.169-175
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• 2014

The ICT (information and communication technology) paradigm shift, including the burgeoning use of mobile, SNS, and smart devices, has resulted in an explosion of data along with lifestyle changes. We have thus arrived at the age of big data. In the meantime, a number of difficulties have arisen in terms of cost or on the technical side with respect to the use of large quantities of data. However, big data has begun to receive attention with the advent of efficient big data technologies such as Hadoop. In this paper, we discuss the patent analysis of big data platform technology research and development in major countries. Especially, we analyzed 2,568 patent applications and registered patents in four countries on December 2010.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.64-73
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• 2015

Recent emergence of spatial big data attracts the attention of various research groups. This paper analyzes the research trend on spatial big data by text mining the related Scopus DB. We apply topic model and network analysis to the extracted abstracts of articles related to spatial big data. It was observed that optics, astronomy, and computer science are the major areas of spatial big data analysis. The major topics discovered from the articles are related to mobile/cloud/smart service of spatial big data in urban setting. Trends of discovered topics are provided over periods along with the results of topic network. We expect that uncovered areas of spatial big data research can be further explored.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.207-218
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• 2018

Recently, it becomes a big trend in the banking industry to apply a big data analytics technique to extract essential knowledge from their customer database. Such a trend is based on the capability to analyze the big data with powerful analytics software and recognize the value of big data analysis results. However, there exits still a need for more systematic theory and mechanism about how to adopt a big data analytics approach in the banking industry. Especially, there is no study proposing a practical case study in which big data analytics is successfully accomplished from the marketing perspective. Therefore, this study aims to analyze a target marketing case in the banking industry from the view of big data analytics. Target database is a big data in which about 3.5 million customers and their transaction records have been stored for 3 years. Practical implications are derived from the marketing perspective. We address detailed processes and related field test results. It proved critical for the big data analysts to consider a sense of Veracity and Value, in addition to traditional Big Data's 3V (Volume, Velocity, and Variety), so that more significant business meanings may be extracted from the big data results.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.101-115
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• 2020

In this paper, big data analysis method was used to find out global trends in autonomous driving and to derive activate spatial information services. The applied big data was used in conjunction with news articles and patent document in order to analysis trend in news article and patents document data in spatial information. In this paper, big data was created and key words were extracted by using LDA (Latent Dirichlet Allocation) based on the topic model in major news on autonomous driving. In addition, Analysis of spatial information and connectivity, global technology trend analysis, and trend analysis and prediction in the spatial information field were conducted by using WordNet applied based on key words of patent information. This paper was proposed a big data analysis method for predicting a trend and future through the analysis of the connection between the autonomous driving field and spatial information. In future, as a global trend of spatial information in autonomous driving, platform alliances, business partnerships, mergers and acquisitions, joint venture establishment, standardization and technology development were derived through big data analysis.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.251-257
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• 2018

As the use of big data is necessary for increasing business value, the size of the big data market is getting bigger. Accordingly, it is important to apply competitive patents in order to gain the big data market. In this study, we conducted the patent analysis based keyword network to analyze the trend of big data patents. The analysis procedure consists of big data collection and preprocessing, network construction, and network analysis. The results of the study are as follows. Most of big data patents are related to data processing and analysis, and the keywords with high degree centrality and between centrality are "analysis", "process", "information", "data", "prediction", "server", "service", and "construction". we expect that the results of this study will offer useful information in applying big data patent.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.359-367
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• 2019

As a strategy for strengthening local competitiveness, efficient use of regional assets is becoming more and more important. The key to regional identity and competitiveness is local assets. The purpose of this study is to derive the priority region for development by evaluating local assets. The analysis methods used in this study are Geographic Information System analysis, Big Data Trend analysis, and Analytic Hierarchy Process analysis. To assess the potential of local assets, the preference of assets, historical value, cluster of resources, wide-area transport accessibility, and population density were set as analysis indicators and itemized weights were applied using AHP to reflect the importance of each item. As a result of analyzing Yeongju city in Gyeongsangbuk-do, eight major points such as Buseoksa Temple, Sosu Seowon, Huibangsa Temple, Punggi Hot Spring Resort, Punggi Station, National Center for Forest Therapy, Yeongju east region and Museom Village were derived.

• 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 Technology Innovation
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• v.26 no.4
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• pp.57-97
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• 2018

Big Data has spread rapidly in various industries since 2010. We analyzed the general characteristics of big data through time series analysis on the initial process of spreading big data and investigated the difference of diffusion characteristics in each industry. By analyzing papers, patents, news data, and Google Trend using Big Data as a keyword, we searched for data corresponding to the leading indicator, and confirmed that trends in news and Google Trend preceded the papers and patents by two years. We used Google Trend to compare the introduction period of domestic, US, Japan, and China and quantify the process of spreading the eight main industries in Korea through news data. Through this study, we present an empirical research method on how the general technology spreads in several industry sectors and we have figured out where the spreading speed difference of big data originated in each industry in Korea. The method presented here can be used to analyze the technology introduced from foreign countries in developing countries because it can be analyzed in diffusion process of other technologies besides big data and corresponds to the diffusion of technology keywords in a specific country. And, on the corporate side, this approach shows what path is effective when it comes to launching and spreading new technologies.

• Information Systems Review
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• v.20 no.2
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• pp.63-85
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• 2018

Emerging hotspot and trendy areas are formed into alleys and blocks with the help of viral effects among social network services (SNS) users called "Golmogleo." These users search for every corner of the alleys to share and promote their own favorite places through SNS. An analysis of hot places is limited if it is only based on macroeconomic indicators such as commercial area data published by national organizations, large-scale visiting facilities, and commuter figures. Careful analyses based on consumers' actual activities are needed. This study develops a "social big data analysis methodology" using Instagram data, which is one of the most popular SNSs suitable to identify recent consumer trends. We build a spatial analysis model using Local Moran's I. Results show that our model identifies new trend zones on the basis of posting data in Instagram, which are not included in the commercial information prepared by national organizations. The proposed analysis methodology enables better identification of the latest trend areas formulated by SNS user activities. It also provides practical information for start-ups, small business owners, and alley merchants for marketing purposes. This analytical methodology can be applied to future studies on social big data analysis.