• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.732-733
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• 2014

This study aims to examine the R&D trends on Big Data sector through patent analysis and to suggest directions of the R&D activities in Korea. According to the results of analysis, the R&D trends of Big Data sector have shown two characteristics. First, the US has monopolized the world market of Big Data Sector. The patent activities of US have shown relatively even throughout every technology. And the average share of each technology is over 40%. Second, the trends of R&D have been changed. In the past, data analysis and processing technologies were the mainstream, whereas data operations and management technologies are mainly featured. However, the patent applications in Korea have been concentrated on storage technologies, while the applications for data operations and management technologies are correspondingly low; therefore, it seemingly needs urgent research and development of relevant technologies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.66-67
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• 2014

This study is to compare and analyze the technology competitiveness of Big Data sector through patent analysis. According to results of the analysis, the United States is the most active country on the patent activities and its applied and registered patents have multiplied in recent times. However, both CPP(Cited Per Patent) and PII(Patent Impact Index) of the US would have declined, indicating that the growth of technology in the US seems to be biased to quantitative growth of technologies. Korea has been relatively active patent activities with Japan, China and Canada, whereas it has tended to be biased to quantitative growth with the US; Therefore, Korea government needs to concentrate efforts to retain the high quality technologies.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.5-6
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• 2019

빅데이터 분석을 위해 활용되는 데이터로는 뉴스, 블로그, SNS, 논문, 특허 그리고 센서로부터 수집된 데이터 등 매우 다양한 유형의 데이터가 있다. 특히, 신뢰성 있는 데이터를 실시간 제공하는 웹 데이터의 활용이 점차 확산되고 있다. 그리고 빅데이터의 활용이 다양한 분야로 점차 확산되고 웹 데이터가 매년 기하급수적으로 증가하면서, 최근 웹 데이터는 재난대응 미디어로써 매우 중요한 역할을 하고 있다. 또한, 빅데이터 분석에 활용되는 원천 데이터는 네트워크 형태이며, 최근 소셜 네트워크 분석을 통한 효과적인 상품 광고, 핵심 유전자 발굴, 신약 재창출 등 다양한 영역에서 네트워크 분석 기술이 사회와 인류에게 가치 있는 정보를 제공할 수 있는 가능성을 제시하면서 네트워크 분석 기술의 중요성이 부각되고 있다. 본 논문에서는 웹에서 제공하는 뉴스와 SNS 데이터를 이용해 수난 발생 및 규모 예측을 지원하는 웹 크롤러 및 네트워크 분석기술을 제안한다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.367-368
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• 2023

4차 산업혁명으로 다양한 기술과 아이디어가 생겨나고 있고, 이를 보호하기 위한 특허는 그 등록 건수가 매년 증가하는 추세이다. 그러나 현재 특허문서를 분류하는 과정을 수동으로 진행하고 있기에 이를 자동으로 진행할 수 있는 분류기를 생성할 필요를 느꼈고, 본 논문에서는 특허문서를 분류기에 적용할 데이터의 전처리 과정 중 데이터 변환과 통합 과정을 다루었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.393-396
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• 2022

본 연구는 국방과학기술 분야의 특허 및 논문 실적을 이용하여 통계기반 기계학습 모델 4 종을 학습하고, 실제 분석 대상기관의 데이터 입력결과를 분석하여 실용성에 대한 한계점 분석을 목적으로 한다. 기존 연구에서는 특허분류코드를 기준으로 분류하여 특수 목적으로 활용하거나 세부 연구 범위 내 연구 주제탐색 및 특징연구 등 미시적인 관점에서의 상세연구 활용 목적인 반면, 본 연구는 거시적인 관점에서 연구의 전체적인 흐름과 경향성 파악을 목적으로 한다. 이에 ICT 기술 138 종의 특허 및 논문 30,965 건과 국방과학기술 192 종의 특허 및 논문 23,406 건을 학습데이터로 각 모델을 학습하였다. 비교한 통계기반 학습모델은 Support Vector Machines, Decision Tree, Naive Bayes, XGBoost 모델이다. 학습데이터에 대한 학습검증 단계에서는 최대 99.4%의 성능을 보였다. 다만, 실제 분석대상기관의 특허 및 논문 12,824 건으로 입력분석한 결과, 모델별 편향성 문제, 데이터 전처리 이슈, 다중클래스 및 다중레이블 문제를 확인, 도출한 문제에 대한 해결방안을 제시하고 추가 연구의 방향성을 제시한다.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.1-8
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• 2020

The use of text data in big data analytics has been increased. So, much research on methods for text data analysis has been performed. In this paper, we study Bayesian learning based on conjugate prior for analyzing keyword data extracted from text big data. Bayesian statistics provides learning process for updating parameters when new data is added to existing data. This is an efficient process in big data environment, because a large amount of data is created and added over time in big data platform. In order to show the performance and applicability of proposed method, we carry out a case study by analyzing the keyword data from real patent document data.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1025-1039
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• 2016

We introduce various methods to investigate the relations between innovation of technology and social changes by analyzing more than 4 millions of patents registered at United States Patent and Trademark Office(USPTO) from year 1985 to 2015. First, we review the history of patent law and its relation with the quantitative changes of registered patents. Second, we investigate the differences of technical innovations of several countries by use of cluster analysis based on the numbers of registered patents at several technical sectors. Third, we introduce the PageRank algorithm to define important nodes in network type data and apply the PageRank algorithm to find important technical sectors based on citation information between registered patents. Finally, we explain how to use the canonical correlation analysis to study relationship between technical innovation and social changes.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.418-431
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• 2022

Purpose: We propose a new scheme for predicting promise technology and it improves the conventional scheme that misses important lists of patent because of insufficient search formula, and cannot reflect new trend of technology due to the unreleased period of patents. Method: In this paper, we propose a new search formula exploiting TF and TF-IDF with R programming as well as related keywords, and LDA topic modeling scheme is used for analyzing recently published papers in Satellite·Terrestrial Information Convergence Disaster Response. Result: By comparing both schemes with commercial DB, the proposed scheme can find more important patents, and can reflect new trend of technology, compared to the conventional scheme. Conclusion: The proposed scheme can be used to predict promise technologies in the field of Satellite·Terrestrial Information Convergence Disaster Response.