Due to sudden transition to intellectual society corresponding with fast technology progress, companies and nations need to focus on development and guarantee of intellectual property. The possession of intellectual property has been the important factor of competition power. In this paper we developed the efficient patent search process with big data analysis tool R. This patent search process consists of 5 steps. We result that at first this process obtain the core patent search key words and search the target patents through search formula using the combination of above patent search key words.

The purpose of this study is to prove that new values for the agricultural food area can be created by combining various big data collected in the agricultural food area and analyzing them in an appropriate analysis method. For this, the analysis techniques generally used were studied, and the use of the big data in the various areas of the current society was explored through practical application instances. In addition, by the current status and analysis instances of the big data use in the agricultural food area, this study was conducted to verify how the new values found were being used.

디지털 기술의 발달로 세계가 정보 및 지식이 주도하는 사회로 급변하고, 지식 재산권의 발전이 급속하게 진행되면서, 각 기업 및 국가들은 그들의 경쟁력을 키우기 위해 지식재산권에 대한 중요성을 강조하고 있다. 이와 같이 지식재산권의 중요성이 강조되는 현실에서 지식재산권의 확보는 기업의 경쟁력을 좌우하는 요소라 할 수 있다. 따라서 본 논문에서는 빅데이터 분석 도구인 R을 이용하여 빠른 시간 안에 사용자가 목적으로 하고 있는 특허검색 결과를 효율적으로 도출할 수 있는 검색어 추출에 관한 연구를 진행하였다. 이를 위해 다섯 단계의 특허 검색 프로세스를 제안하였고 프로그램으로 구현하여 검색목적에 맞는 특허의 검색에 필요한 시간을 대폭 단축시키면서 목표로 하는 특허 검색을 효율적으로 할 수 있었다.

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.

The paper aims to facilitate a discussion around how big data technologies and data from citizens can be used to help public administration, society, and policy-making to improve community’s lives. This paper discusses opportunities and challenges of big data strategies for government, society, and policy-making. It employs the presentation of numerous practical examples from different parts of the world, where public-service delivery has seen transformation and where initiatives have been taken forward that have revolutionized the way governments at different levels engage with the citizens, and how governments and civil society have adopted evidence-driven policymaking through innovative and efficient use of big data analytics. The examples include the governments of the United States, China, the United Kingdom, and India, and different levels of government agencies in the public services of fraud detection, financial market analysis, healthcare and public health, government oversight, education, crime fighting, environmental protection, energy exploration, agriculture, weather forecasting, and ecosystem management. The examples also include smart cities in Korea, China, Japan, India, Canada, Singapore, the United Kingdom, and the European Union. This paper makes some recommendations about how big data strategies transform the government and public services to become more citizen-centric, responsive, accountable and transparent.

This study analyzes the key drivers (commitment, integration of big data, green supply chain management, and green human resource practices) of sustainable capabilities and the influence to which these sustainable capabilities impact the banks’ environmental and financial performance. Additionally, this study analyzes the impact of green management practices on the integration of big data technology with operations. The theory of dynamic capability was deployed to propose and empirically test the conceptual model. Data was collected through a self-administrated survey questionnaire from 319 participants employed at 35 banks located in six ASEAN countries. The findings indicate that big data analytics strategies have an impact on internal processes and banks’ sustainable and financial performance. This study indicates that banks committed towards proper data monitoring of its clients achieve operational efficiency and sustainability goals. Moreover, our results confirm that banks practising green innovation strategies experience better environmental and economic performance as the employees of these banks have received advance green human resource training. Finally, our study found that internal and external green supply chain management practices have a positive impact on banks’ environmental and financial performance, which confirms that ASEAN banks contributing in reduction of environmental impact through its operations will ultimately experience increased financial performance.

This study aims to recognize customers’ real sentiment and then discover the data-driven insights for strategic decision-making in the financial sector of Saudi Arabia. The data was collected from the social media (Facebook and Twitter) from start till October 2018 in financial companies (NCB, Al Rajhi, and Bupa) selected in the Kingdom of Saudi Arabia according to criteria. Then, it was analyzed using a sentiment analysis, one of data mining techniques. All three companies have similar likes and followers as they serve customers as B2B and B2C companies. In addition, for Al Rajhi no negative sentiment was detected in English posts, while it can be seen that Internet penetration of both banks are higher than BUPA, rarely mentioned in few hours. This study helps to predict the overall popularity as well as the perception or real mood of people by identifying the positive and negative feelings or emotions behind customers’ social media posts or messages. This research presents meaningful insights in data-driven approaches using a specific data mining technique as a tool for corporate decision-making and forecasting. Understanding what the key issues are from customers’ perspective, it becomes possible to develop a better data-based global strategies to create a sustainable competitive advantage.

With the recent global urban issues such as climate change, urbanization, and energy problems, the smart city was proposed as one of the solutions in urban planning. This study introduces the smart city initiatives of South Korea by examining the recent history of smart city policies and their limitations. This case study reflects the experience of one of the countries which thrived to building smart cities as their national key industries to drive economic growth. It also analyzes the trends of the smart city using big data analysis techniques. Although there are obstacles such as economic recession, failing to differentiate from the U-city, low service level than expected smart functionality, We could recognize the current status of the smart city policies in South Korea such as 1) Korean smart city development projects are actively implemented, 2) public consensus suggests that applying advanced technology and the active role of government need, 3) a comprehensive and strategic approach with the integration and application of advanced technologies is required as well, 4) investment by both private and public sectors need to deliver social improvements. This study suggests future direction of smart city polity in South Korea in the conclusion.

This study had two main objectives. We first investigated which weather phenomena people were most concerned about in the context of climate change or global warming. Then, we conducted content analysis to find which words were more commonly used with climate change or global warming. For this, we collected web data from Twitter, Naver, and Daum from April to October 2019 in the Republic of Korea. The results suggested that people were more concerned about air quality, followed by typhoons and heat waves. Because this study only considered one warm period in the year of 2019, winter-related weather phenomena such as cold wave and snowfall were not well captured. From Twitter, we were able to find wider range of terminologies and thoughts/opinions than Naver and Daum. Also, more life-relevant weather events such as typhoons and heat waves in Twitter were commonly mentioned compared to Naver and Daum. On the other hand, the comments from Naver and Daum showed relatively narrower and limited terms and thoughts/ opinions. Especially, most of the comments were influenced by headlines of articles. We found many comments about air quality and energy/economic policy. We hope this paper could provide background information about how to promote the climate change education and public awareness and how to efficiently interact with general audiences.

최근 공공시설물의 노후화에 따른 사회문제가 빈번하게 발생하고 있으며, 이에 따라 시설물에 대한 국민의 불안감도 증가 하고 있다. 향후 10년 내에 급증하게 되는 시설물 노후화 문제의 효과적인 대응을 위해 현재의 사후적인 유지관리에서 예측을 통한 선제적 유지관리로의 패러다임 전환이 시급한 실정이다. 본 연구에서는 빅데이터 시범분석을 통해서 교량, 터널, 공공건축물 일부에 대해 FMS 축적된 유지관리 데이터를 활용하여 지역별·환경별·공용년수별 취약요소를 도출하였고, Social Media의 비정형 데이터 분석을 통해 국민이 체감하는 불안/불편요소를 도출하였다. 또한 교량 취약요소의 손상발생패턴 분석을 통해 향후 선제적으로 관리해야 하는 유지관리 항목 및 추가적으로 확보해야 하는 디지털 정보 등을 제안하였다.

성수동은 1960년대에 발전한 준공업지역으로 제조업이 중심 사업이었으나, 산업 경쟁력 약화로 토착 산업이 빠른 속도로 쇠퇴하고 있다. 지역 재생을 위하여 지역브랜드가 장소 마케팅의 수단으로 활용되고 있다. 지역브랜드 발전 방향을 제시하기 위해 성수동의 현 소비자 인식을 파악할 필요가 있다. 이를 위해, 데이터 분석 프로그램인 텍스톰(Textom)을 사용하였다. 검색어는 ‘성수동’을 사용하였으며, 네이버의 블로그와 뉴스를 대상으로 수집하였다. 수집 기간은 2018년 8월 1일부터 2019년 7월 31일까지의 데이터를 대상으로 텍스트 마이닝을 실시하였다. 빈도분석 결과 ‘서울, 카페, 맛집’이 빈도가 높았으며, TF-IDF분석 결과 ‘카페, 맛집, 포토’가 주요 단어임을 알 수 있었다. 또한, UCINET6과 NetDraw를 사용하여 네트 워크 분석, CONCOR분석을 실시하였다. 분석 결과, 성수동과 관련하여 카페와 맛집, 분위기와 공간 등을 다룬 클러스터와 성수동에서 진행한 행사와 관련된 클러스터로 크게 2갈래로 나뉘었다. 성수동이 지역브랜드로서 브랜드 이미지를 강화 하기 위해서, 성수동에 대한 인식에서 대표적 키워드인 ‘카페’ 등 요식업의 공간 기획, 외식 문화 기획 시 ‘공장’이라는 차별성을 접목하는 방향으로 지역브랜드 강화에 힘쓸 것을 제안한다.

This study is to explore the relationship between the Fourth Industrial Revolution and the environment using the big data methodology. We scrutinize the trend of the Fourth Industrial revolution, in association with the environment, and provide implications for a more desirable future environmental policy. The results show that the Industrial Revolution has been generally perceived as negative to environment before the 2010s, while it has been widely regarded as positive after the period. It is highly expected that the Fourth Industrial Revolution will be capable of functioning as a new alternative to enhance the quality of the biophysical and social environment. This study justifies that the new wave of technological development may serve as a cure for the enhancement of the environmental quality. The positive linkage between the new technological development and the environment from this study clearly indicates that the environmental industry and environmental technologies will be key economic factors in the next-generation society. They should be of critical importance in shaping our cities into clearer and greener spaces, and people will continuously depend on the development of new environmental technologies in order to correct environmental damages.

This study investigated the relationship between heat-related illnesses obtained from healthcare big data and daily maximum temperature observed in seven metropolitan cities in summer during 2013~2015. We found a statistically significant positive correlation (r = 0.4~0.6) between daily maximum temperature and number of the heat-related patients from Pearson's correlation analyses. A time lag effect was not observed. Relative Risk (RR) analysis using the Generalized Additive Model (GAM) showed that the RR of heat-related illness increased with increasing threshold temperature (maximum RR = 1.21). A comparison of the RRs of the seven cities, showed that the values were significantly different by geographical location of the city and had different variations for different threshold temperatures. The RRs for elderly people were clearly higher than those for the all-age group. Especially, a maximum value of 1.83 was calculated at the threshold temperature of 35℃ in Seoul. In addition, relatively higher RRs were found for inland cities (Seoul, Gwangju, Daegu, and Daejeon), which had a high frequency of heat waves. These results demonstrate the significant risk of heat-related illness associated with increasing daily maximum temperature and the difference in adaptation ability to heat wave for each city, which could help improve the heat wave advisory and warning system.

This study analyze the economic effect of chemical fertilizer. We used the input and output data, and the analysis variables include production output nitrogen, phosphoric acid, potassium, seeds, and labor. The main results are as follows. First, for spring potatoes, potassium increases to a certain level of output, but over a certain stage, the output decreases as the input increases. Optimal use of potassium in the calculation of spring potatoes can achieve the effect of reducing input costs and increasing output simultaneously. Second, radish In autumn, nitrogen increases to a certain level, but over a certain stage it represents a reverse U-shaped relationship in which output decreases as input increases. This means that reducing the amount of fertilizer input increases the output. This means that soil-related agricultural big data can contribute to the management of nutrients and greenhouse gas reduction in agricultural land.

This paper proposed the measures that to predict changes in the state of the individual tunnels, and maintenance costs during its life cycle by using the big data of tunnel facilities. This is expected to be used to efficiently establish long term maintenance plans for tunnels based on data-based engineering analysis.

최근 빅 데이터는 4차 산업 혁명시대에 주요한 패러다임으로 주목받고 있다. 기업의 빅 데이터 활용은 고객의 행동을 선 예측하여 기업의 경쟁력을 강화시키고, 생산성 향상과 비즈니스 혁신을 가능하게 한다는데 의미가 있으며, 이는 패션 분야 에 있어서도 예외가 아니다. 본 연구는 국내 패션부문에서 활용되고 있는 빅 데이터에 관한 연구로서 연구의 목적은 국내 패션업계의 빅 데이터 활용에 대한 실제적인 동향을 사례를 중심으로 파악하고자 하였다. 결론적으로 국내 패션관련 부문의 빅 데이터 활용에 관한 실제 동향은 ‘빅 데이터 활용 초기단계의 한계성과 현실적인 운 용방식의 채택’으로 압축할 수 있었으며, 이는 각 사의 사업영역과 업태를 기초로 그 내용을 분류할 수 있었다. 다시 말해, 현재 패션관련업계의 빅 데이터는 활용 초기단계라는 시간과 비용의 한계성에 의해 데이터 수집과 분석의 범위에 있어 제 한적일 수밖에 없다. 따라서 빅 데이터의 분석결과를 사업에 이용하는 업체는 그 활용 목적에 따라 로그 분석, 딥러닝, 텍 스트 마이닝 등 적합한 데이터의 활용 및 분석의 기법을 선택하여 현실적인 범주 내에서 적절하게 운용하고 있음을 연구를 통해 확인할 수 있었다.

Many of the structures constructed since the 1970s have begun to deteriorate. As a result, safety inspection / diagnosis and maintenance are expected to increase further, and long-term and systematic measures are urgently needed to prepare for the aging of major infrastructures. Currently, the safety inspection and maintenance of the structure is carried out by the method based on the utilization of the measurement data and the manager's experience, but the reliability and the accuracy are still insufficient. In other words, there is a limit to understanding the state change of bridges, such as abnormal behavior and gradual damages, therefore, it is necessary to have an accurate and reliable monitoring process using new techniques and systems. For this reason, I would like to examine the possibility of using cloud and big data in the field of infrastructure safety management.

This study is exploring objective awareness of forest therapy by consideration of popular perception about forest therapy through analysis of big data. The purpose of this study is the deduction of meaning information through analysis in the viewpoint of big data at online social network service (SNA) about ‘forest therapy’. Accordingly, their main way of research became contents analysis of keyword linked to forest therapy. The study mainly grasped ‘forest therapy’ and analyzed ‘forest healing’, ‘forest bathing’, ‘forest interpreter’ comparatively. The period of study was from Sep. 8th to Oct. 8th, 2015 (during 30 days), and SNA such as blog or twitter became the subject of search. First, awareness of the forest therapy on SNS has been talked about a lot of properties (forest healing, phytoncide, stress, experience, health) or places (forest, seoul) psychology-related terms (various, stress). Second, public opinion on these terms could be found that there were plenty of positive public opinion than neutral. Third, the keywords related to the forest healing could be found that there were differences, depending on the subject to experience the program and ongoing environment. This study is significant in that the awareness among the general public about the forest healing were searched and it could provide an alternative for the activation of forest healing. We expect this study to be a starting point of research utilizing Big Data in the field of forest therapy and to be used as the basis for forest healing policy, program development, public relations and marketing by identifying and recognizing the characteristics of forest healing of SNS era.

현재까지 모바일 게임 사용자 연구는 개별 콘텐츠의 재미, 중독성, 편의성과 같은 1차적 정서 를 분석하는 차원에 머물러 있다. 그러나 스마트폰의 확산 이후 사용자들의 멀티태스킹이 보편 화되면서 사용자의 게임 콘텐츠 경험은 복잡해지고 있다. 따라서 다양한 행위를 동시에 수행하 는 사용자의 관점에서 모바일 게임에 대한 보다 깊이 있는 분석이 필요한 상황이다. 본 연구는 집단 감성의 관점에서 모바일 게임 사용자들의 연결된 심성 모형을 포착하고자 했다. 이를 위 해 사용자들의 비의도성과 의도성을 동시에 포착할 수 있는 소셜 데이터 분석을 실시했으며, 그 결과로 서비스의 교차 소비, 정보 추천방식의 다양화, 관계 기반의 과제 경험을 주요 이슈로 제시했다.

최근 인터넷, 스마트기기의 발달과 소셜 미디어의 등장으로 데이터가 기하급수적으로 증가하는 빅데이터 시대가 도래하였다. 빅데이터는 IT 기기 및 사회발전과도 상호 연관적이다. 기존에 예측, 분석이 불가능하였던 데이터들을 여러 분석방법을 통하여 의미있는 데이터를 획득하기 위하여 여러 분야에서 연구되어지고 있다. 가뭄의 경우 발생범위와 심도를 예측하기 힘든 자연재해 중 하나이다. 본 연구에서는 가뭄심도를 파악하기 위하여 빅데이터 분석기법 중 데이터 마이닝(Data Mining)과 구글 트랜드(Google Trend)를 적용하였다. 이때, 구글 트랜드는 가뭄과 관련된 키워드(가뭄, Drought)를 분석하였고, 데이터 마이닝은 국내 3개 언론매체(경향신문, 매일신문, 연합뉴스)의 자료를 토대로 하였다. 빅데이터를 이용한 가뭄해석의 적정성을 평가하기 위하여 전국 6개 광역시, 2개 특별자치지구의 최근 4년간의 강수량을 바탕으로 표준강수지수(SPI)를 산정하고 구글 트랜드 결과와 비교분석 하였다. 본 연구를 통해 향후 가뭄에서의 빅데이터 활용 가능성을 확인하였고 관련 연구 및 방재정책수립의 기초자료로서 사용되길 기대한다.