• 디지털산업정보학회논문지
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• 제11권1호
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• pp.157-169
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• 2015

In digital marketing, means of public relations and marketing of enterprises are changing into marketing techniques of predictive analytics. A significant study can be carried out by an analysis of 'the patterns of customers' uses' using big data on major portal sites and SNSs and their correlation with related keywords. This study analyzes the origins of mythological characters in major brands such as Nike, Hermes, Versace, Canon and Starbucks. Also, it extracts related keywords and relevance using big data on portal sites and SNS and their correlation. Nike marketing that reminds people of 'the goddess of victory, Nike' formed a good combination of the brand with relevance. Most of them are based on Greek mythology and have rich materials for storytelling and artistic values in common. Hopefully, this case analysis of foreign brands would become a starting point of discovering the materials of the domestic mythological characters.

• 품질경영학회지
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• 제51권4호
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• pp.551-571
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• 2023

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.

• 융합보안논문지
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• 제21권2호
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• pp.19-28
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• 2021

산업의 디지털 전환을 촉진하고 혁신을 가속화하고자 우리나라는 디지털 뉴딜 정책을 추진하고 있다. 그러나 엄격한 기존의 데이터 관련 법제 하에서는 디지털 뉴딜정책을 위한 산업계의 데이터 활용에 여전히 제약이 있는 상황이다. 이러한 문제를 해결하기 위해서 데이터 3법 개정안이 발의되었으나 실제로 산업계의 데이터 이용 활성화에 어떠한 영향을 미칠지에 대한 논의는 아직 부족한 상황이다. 이에 본 연구에서는 데이터 3법에 대한 여론의 인식을 분석하여 데이터 3법 개정안의 시사점을 분석하고자 한다. 이를 위하여 데이터 3법 개정안과 관련 연구동향을 분석하고, 빅데이터 분석 기법을 이용하여 데이터 3법에 대한 인식을 분석하였다. 분석결과에 따르면 데이터 3법은 개정 취지에 부합하게 데이터 산업 활성화를 촉진하는 반면에 특정산업 분야에 치중될 우려가 있는 것으로 나타났다. 본 연구의 결과는 빅데이터 분석을 통해 시행 초기인 데이터 3법의 산업영향에 대한 온라인 인식을 분석함으로써 향후 개선방안에 대한 시사점을 제공한다는 점에서 의의가 있다.

• 전기학회논문지
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• 제63권8호
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• pp.1111-1115
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• 2014

With the increasing interest of a demand side management using a Smart Grid infrastructure, the demand resources and energy usage data management becomes an important factor in energy industry. In addition, with the help of Advanced Measuring Infrastructure(AMI), energy usage data becomes a Big Data System. Therefore, it becomes difficult to store and manage the demand resources big data using a traditional relational database management system. Furthermore, not many researches have been done to analyze the big energy data collected using AMI. In this paper, we are proposing a Hadoop based Big Data system to manage the demand resources energy data and we will also show how the demand side management systems can be used to improve energy efficiency.

• 한국엔터테인먼트산업학회논문지
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• 제14권4호
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• pp.145-153
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• 2020

빠르게 변화하고 있는 4차 산업 시대에 빅데이터는 다양한 분야에 활용되고 있다. 최근 문화예술콘텐츠 전반에도 빅데이터의 활용은 급속도로 적용되고 있고, 그중에서도 영화는 자본이 많이 드는 예술장르로서 빅데이터의 활용은 매우 유용한 분석 수단이다. 본 연구는 2019년 제72회 칸 영화제의 황금종려상과 아카데미 시상식에서 4관왕(작품상, 감독상, 각본상, 외국어 영화상)을 차지하며 한국영화의 가치를 보여준 영화 <기생충>을 대상으로 빅데이터 분석기법을 적용하여 실시하였다. 이렇게 분석된 값은 데이터의 주기별 변화량과 감성의 값을 부여하는 오피니언 마이닝을 통해 영화 흥행을 예측하고, 페이스북(Facebook), 트위터(Twitter) 등 SNS의 활용지수와 토픽 키워드를 추출하여 관객들의 관심을 반영하는 영화적 요인들이 무엇인지를 살펴보았다. 이처럼 빅데이터를 활용한 영화흥행 요인분석으로 모델 구축 및 모형 개발로 흥행예측이 가능해지면 영화제작 과정의 효율성을 극대화하면서 제작비용과 영화실패에 따른 리스크를 최소화 할 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• 한국해양환경ㆍ에너지학회지
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• 제20권2호
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• pp.117-125
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• 2017

SNS 빅데이터 분석은 소셜 미디어에서 생성되는 빅데이터로부터 숨겨진 가치를 찾아내는 것을 의미한다. 본고는 해양수산 분야의 국민적 관심사를 파악하기 위해 24개 키워드를 도출하여 SNS 빅데이터 분석을 실시하였다. 언급량이 많은 키워드는 수산물, 해운, 독도 순이었으며, 해양정책, 해양안보 등 국민적 관심사가 적은 키워드는 상대적으로 언급량이 미미했다. 매체별 언급량은 정부가 주도하는 분야는 뉴스에, 민간이 주도하거나 국민생활 연관성이 큰 경우는 블로그와 트위터에 많았다. 따라서 해양수산 정책 수립 시 SNS 빅데이터 분석을 활용해 국민적 관심사를 반영하고, 특히 부정적인 요인을 해소하는데 역점을 두어야 한다. 또한 매체별로 언급량이 다르므로 차별화된 홍보방안을 마련할 필요가 있다.

• 한국의류학회지
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• 제41권2호
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• pp.378-391
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• 2017

The application of Big Data has been introduced to the Korean fashion industry; however, the literature has not yet investigated how well high technologies are being perceived and adopted by the practitioners of fashion companies. Recognizing the lack of research, the current research explores how big data analysis has been adopted by fashion practitioners based on the Technology Acceptance Model (TAM) that considers the effect of organizational characteristics (i.e., innovation, slack, and IS infra maturity). First, all TAM relationships were accepted as significant; however, the effect of perceived ease of use on the attitude toward big data was greater than perceived usefulness. Regarding organizational characteristics, while organization innovation had positive impacts on perceived usefulness as well as perceived ease of use, organization slack did not show significant and positive influence on perceived ease of use only. On the other hand, IS infra maturity had a negative effect on perceived usefulness while it did not have any significant impact on perceived ease of use. Finally, the level of perceived usefulness is decreasing as the IS infra of the fashion organization becomes more mature. With the results, the study suggested that fashion industry needs more education on the usage of big data analysis systems and development in related analysis tools.

• 대한안전경영과학회지
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• 제14권4호
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• pp.229-238
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• 2012

Within last 10 years, internet has become a daily activity, and humankind had to face the Data Deluge, a dramatic increase of digital data (Economist 2012). Due to exponential increase in amount of digital data, large scale data has become a big issue and hence the term 'big data' appeared. There is no official agreement in quantitative and detailed definition of the 'big data', but the meaning is expanding to its value and efficacy. Big data not only has the standardized personal information (internal) like customer information, but also has complex data of external, atypical, social, and real time data. Big data's technology has the concept that covers wide range technology, including 'data achievement, save/manage, analysis, and application'. To define the connected technology of 'big data', there are Big Table, Cassandra, Hadoop, MapReduce, Hbase, and NoSQL, and for the sub-techniques, Text Mining, Opinion Mining, Social Network Analysis, Cluster Analysis are gaining attention. The three features that 'bid data' needs to have is about creating large amounts of individual elements (high-resolution) to variety of high-frequency data. Big data has three defining features of volume, variety, and velocity, which is called the '3V'. There is increase in complexity as the 4th feature, and as all 4features are satisfied, it becomes more suitable to a 'big data'. In this study, we have looked at various reasons why companies need to impose 'big data', ways of application, and advanced cases of domestic and foreign applications. To correspond effectively to 'big data' revolution, paradigm shift in areas of data production, distribution, and consumption is needed, and insight of unfolding and preparing future business by considering the unpredictable market of technology, industry environment, and flow of social demand is desperately needed.

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1259-1264
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• 2019

Nowadays, cloud computing and big data analytics are at the center of many industries' concerns to take advantage of the potential benefits of building future smart cities. The integration of cloud computing and big data analytics is the main reason for massive adoption in many organizations, avoiding the potential complexities of on-premise big data systems. With these two technologies, the manufacturing industry, healthcare system, education, academe, etc. are developing rapidly, and they will offer various benefits to expand their domains. In this issue, we present a summary of 18 high-quality accepted articles following a rigorous review process in the field of cloud computing and big data analytics.