• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4259-4265
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• 2012

Digital technologies and raising problems in freeform building design and construction in Korea were examined in this paper. Three Korean building cases were researched by having interviews with experts and documentary survey for this purpose. The following problems and important points were drawn from this research. The necessity of panel optimization, significance of the secure file conversion, difficulties in securing constructability of freeform building and using of 3D data for manufacturing panels.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.655-665
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• 2018

Social network services (SNS) have become the most common tool for the communication of public and private opinions as well as public issues; consequently, one may form or drive public opinions to advocate by spreading positive content using SNS. Controversy for survey data based opinion poll accuracy continues in relation to response rate or sampling methodology. This study suggests complementary measures that additionally consider the sentiment analysis results of unstructured data on a social network by data crawling and sentiment dictionary adjustment process. The suggested method shows the improvement of prediction accuracy by decreasing error rates.

• Computational Structural Engineering
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• v.29 no.2
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• pp.12-17
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• 2016

본 기사에서 대형 비정형 건축물의 시공 중 자동 모니터링 사례를 소개하였다. 소개한 사례에서는 무선 자동 계측 시스템을 이용하여 변형률계, 경사계, 변위계 등이 총 88개 설치되어 약 13개월 동안 장기 모니터링이 수행되었다. 무선 자동 계측시스템은 장기간의 데이터를 확보하고 데이터 간의 상관성 및 이력분석을 용이하게 하였다. 이를 통해 대상 구조물의 시공 중 안전성 및 안정성을 확보하고, 정밀 시공을 구현하는데 기여할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.153-155
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• 2017

빅데이터 분석은 데이터베이스에 잘 정리된 정형 데이터뿐 아니라 인터넷, 소셜 네트워크 서비스, 모바일 환경에서 생성되는 웹 문서, 이메일, 소셜 데이터 등 비정형 데이터를 효과적으로 분석하는 기술을 말한다. 대부분의 빅데이터 분석 기술 방법들은 기존 통계학과 전산학에서 사용되던 데이터 마이닝, 기계 학습, 자연 언어 처리, 패턴 인식 등이 이에 해당된다. 글로벌 리서치 기관들은 빅데이터를 2011년 이래로 최근 가장 주목받는 신기술로 지목해오고 있다. 따라서 대부분의 산업에서 기업들은 빅데이터의 적용을 통해 가치 창출을 위한 노력을 기하고 있다. 본 연구에서는 다음 커뮤니케이션의 빅데이터 분석도구인 소셜 매트릭스를 활용하여 2017년 5월, 1개월 시점을 설정하고 "4차 산업혁명" 키워드에 대한 소비자들의 인식들을 살펴보았다. 빅데이터 분석의 결과는 다음과 같다. 첫째, 4차 산업혁명 키워드에 대한 연관 검색어 1위는 "후보"가 빈도수(7,613)인 것으로 나타났다. 둘째, 연관 검색어 2위는 "안철수"가 빈도수(7,297), 3위는 "문재인"이 빈도수(5,183)로 각각 나타났다. 다음으로 "4차 산업혁명" 키워드에 대한 검색어 긍정적 여론 빈도수 1위는 새로운(895)으로 나타났고, 부정적 여론 빈도수 1위는 위기(516)가 차지하였다. 이러한 결과 분석결과를 바탕으로 연구의 한계와 시사점을 제시하고자 한다.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.97-110
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• 2018

With the advent of the 4-th industrial revolution, manufacturing companies have increasing interests in the realization of smart manufacturing by utilizing their accumulated facilities data. However, most previous research dealt with the structured data such as sensor signals, and only a little focused on the unstructured data such as text, which actually comprises a large portion of the accumulated data. Therefore, we propose an association rule mining based facility error pattern extraction framework, where text data written by operators are analyzed. Specifically, phrases were extracted and utilized as a unit for text data analysis since a word, which normally used as a unit for text data analysis, is unable to deliver the technical meanings of facility errors. Performances of the proposed framework were evaluated by addressing a real-world case, and it is expected that the productivity of manufacturing companies will be enhanced by adopting the proposed framework.

• Annual Conference of KIPS
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• 2019.05a
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• pp.501-503
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• 2019

IT 기술의 발달에 따라 다양한 웹 미디어의 데이터가 기하급수적으로 증가하고 있으며 이는 비정형 형태의 빅 데이터로 활용도가 매우 높다. 그 중 인터넷 뉴스나 SNS 등은 시간의 흐름에 따라 다양한 이슈들이 서로 영향을 주며 발생, 결합, 분화, 소멸된다. 본 논문에서는 인터넷상에서 발생하는 비정형 데이터들을 수집하여 텍스트 마이닝을 통해 글의 주요이슈 키워드, 카테고리, 날짜 등을 추출한다. 추출한 데이터를 일정 기간별로 나누어 이슈 매핑을 통해 이슈간의 상관관계를 분석한다. 나아가 LSTM 또는 GRU를 이용한 딥러닝을 통해 앞으로의 이슈를 예측하는 시스템 설계를 제안한다.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.855-864
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• 2004

Programmers can be got the test-related information for implementation without interference of source code complexity by use of the formal specification. Especially the external inputs and system responses can be represented precisely by formal specification in testing phase of web-based software systems. This paper suggests a method of extracting test cases by use of formal specification. Object-Z formal specification represents various test-related information for complex functions of web-based applications. State Transition Models could be built from the formal specification so that test scenarios were extracted from STDs from the highest level to detail levels. The target system for verification of this method is a web-based banking system which is necessary to be secured and critical on errors. This method would be an important factor in automatizing test procedure for web-based application software systems combining the user-base test technique.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.225-230
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• 2016

Recently, Web has arisen large amount of data that to the development of the network and the Internet. In order to process it appeared that Big Data technology. Big Data technologies have been studied aiming a multifaceted and accurate analysis using existing regular data and a variety of data social data. But social data does not have the expertise and objectivity. And such manipulation and concealment and distortion of information have been raised troubling. Thus, this paper proposes for trust big data platform and will be described in detail. The big data platform proposed in this paper consists of data refiner, Data Analyzer, co-truster, visualizer, searcher, etc.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.30-37
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• 2018

The social media data and the broadcasting data related to onion as well as agri-food consumer panel data were collected and investigated if the amount of money spent to purchase onion in year 2014 when onion price plunged latest were correlated with the frequencies of onion-related keywords in the social media data and the broadcasting programs because onion price in year 2018 is expected to plunge due to overproduction and there has been needs to analyze impacts of social media and broadcasting program on onion purchase in the previous similar events, and identify potential factors that can promote onion consumption in advance. What we identified from our study include a) broadcasting news programs mentioning words "onion," were correlated with onion purchase with 3 - 6 weeks in advance; b) broadcasting entertainment programs mentioning words "onion and health," were correlated with onion purchase with 11 weeks in advance; c) blog mentioning words "onion and efficacy," were correlated with onion purchase with 5 weeks in advance. Our study provided a case on how social media and broadcasting programs could be analyzed for their effects on consumer purchase behavior using big data collection and analysis in the field of agriculture. We propose to use the findings from the study may be applied to promote onion consumption.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.133-143
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• 2017

Data is the most important asset in the financial sector. On average, 71 percent of financial institutions generate competitive advantage over data analysis. In particular, in the card industry, the card transaction data is widely used in the development of merchant information, economic fluctuations, and information services by analyzing patterns of consumer behavior and preference trends of all customers. However, creation of new value through fusion of data is insufficient. This study introduces the analysis and forecasting of consumption trends of credit card companies which convergently analyzed the social data and the sales data of the company's own. BC Card developed an algorithm for linking card and social data with trend profiling, and developed a visualization system for analysis contents. In order to verify the performance, BC card analyzed the trends related to 'Six Pocket' and conducted th pilot marketing campaign. As a result, they increased marketing multiplier by 40~100%. This study has implications for creating a methodology and case for analyzing the convergence of structured and unstructured data analysis that have been done separately in the past. This will provide useful implications for future trends not only in card industry but also in other industries.