• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1460-1465
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• 2016

Lots of costs threaten the sustainability of the national health-guarantee system. Despite research by the national center for disease control and prevention on health care dynamics with its auditing systems, there are still restrictions of time limitation, sample limitation, and, target diseases limitation. Against this backdrop, using huge volume of total data, many technologies could be fully adopted to the preliminary forecasting and its target-disease expanding of health. With structured data from the national health insurance and unstructured data from the social network service, we attempted to design a model to predict disease. The model can enhance national health and maximize social benefit by providing a health warning service. Also, the model can reduce the advent increase of national health cost and predict timely disease occurrence based on Big Data analysis. We researched related medical prediction cases and performed an experiment with a pilot project so as to verify the proposed model.

• Industry Promotion Research
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• v.7 no.3
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• pp.17-26
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• 2022

As the well-being mindset to keep our minds and bodies free and healthy more than anything else in the society we live in is spreading, the meaning of health care has become a key part of the 4th industrial revolution such as big data, IoT, AI, and block chain. The advancement of the advanced medical information service industry is being promoted by utilizing convergence technology. In digital healthcare, the development of intelligent information technology such as artificial intelligence, big data, and cloud is being promoted as a digital transformation of the traditional medical and healthcare industry. In addition, due to rapid development in the convergence of science and technology environment, various issues such as health, medical care, welfare, etc., have been gradually expanded due to social change. Therefore, in this study, first, the general meaning and current status of digital health care medical information is examined, and then, developmental tasks to activate digital health care medical information are analyzed and reviewed. The purpose of this article is to improve usability to fully pursue our human freedom.

• Proceedings of the Korean Society for Information Management Conference
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• 2017.08a
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• pp.99-99
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• 2017

최근 4차 산업혁명 시대 도래에 따른 빅데이터가 이슈화 되고 정부의 공공 데이터 개방? 공유 정책 등으로 정부의 R&D 정보화 서비스도 다양화되고 있다. 특히 해양수산 R&D사업은 해양이라는 공간적 제약으로 선박 및 특수 장비 등을 사용함에 따라 연구비 단가가 상대적으로 높은 실정임에도 해양수산 연구 자료 및 관측자료가 통합적으로 관리되지 않고, 사업별 기관별로 산발적으로 관리되고 있어, 이에 따라 연구 DB 통합관리의 수요가 제기 되고 있다. 이에 해양수산 R&D사업에서는 사업별 통합 DB 구축사업이 진행되고 있고, '관할해역해양정보 공동활용시스템(JOISS)'이 대표적이라 할 수 있다. JOISS는 2012년부터 시작된 '관할해역 해양정보 공동활용체계 구축'과제를 통해 자료 표준화 연구와 함께 해양과학조사 분야의 R&D과제들과 실시간 해양관측망으로부터 산출되는 데이터를 수집하고, 정보서비스를 구현한 시스템이다. 2016년 1차 시스템 구축을 완료하여 현재 서비스를 진행하고 있다. 한편, 해양관측 데이터 수집 공유 서비스 외 해양수산 R&D사업과 연계된 다양한 정보들을 나누고 소통하는 온라인 장을 구현하기 위해 '해양수산 R&D 지식정보 시스템(OFRIS)' 개발사업이 별도로 진행되고 있다. OFRIS는 해양수산 R&D사업을 통한 데이터의 원할한 수집 및 품질관리 등의 문제를 보완하고, 그 외에도 사업별로 분산 관리되고 있는 R&D 관련 정보를 연계하고, 기술공급자와 수요자를 직접 연결해 주는 '개방형 기술 정보 중개 시스템'으로의 역할, 국내외 해양수산 R&D관련 정책 연구 산업 동향을 엄선하여 제공하는 등 해양수산 R&D 종합 포털로서 기능구현을 목표하고 있다. 2017년 말 1단계 개발 완료를 앞두고 있으며, 1단계에서는 시급성 높고, 수요가 많은 (1) R&D동향, (2) 과제이력, (3) 연구성과, (4) 기술거래, (5) DB공유 등 5대 기능을 우선 구현하고, 2단계에서는 통계자료 생산 및 분석 기능 강화, 3단계에서는 해양수산 산업통계, 인력, 교육 등의 정보를 서비스하는 포털로 확장할 계획이다. JOISS, OFRIS를 개발하는 과정에서는 해양수산 R&D의 정보를 수집 관리 하는데 있어 다양한 현안 문제 등이 도출되었으며, 그 중에서도 연구자들의 자발적 데이터 제공 협조, 데이터의 표준화 및 품질검증, 구축된 데이터의 활용 및 피드백 등에 대해 구체적이고 현실적인 대응 방안이 요구된다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.157-175
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• 2022

At this moment, a paradigm shift is taking place across all sectors of society for the transition movements to the digital economy. Various movements are taking place in the global agricultural industry to achieve innovative growth using big data which is a key resource of the 4th industrial revolution. Although the government is making various attempts to promote the use of big data, the movement of the agricultural industry as a key player in the use of big data, is still insufficient. Therefore, in this study, effects of performance expectations, effort expectations, social impact, facilitation conditions, based on the Unified Theory of Acceptance and Use of Technology(UTAUT), and innovation tendencies on the acceptance intention of big data were analyzed using the economic and practical benefits that can be obtained from the use of big data for agricultural-related companies as moderating variables. 333 questionnaires collected from agricultural-related companies were used for empirical analysis. The analysis results using SPSS v22.0 and Process macro v3.4 were found to have a significant positive (+) effect on the intention to accept big data by effort expectations, social impact, facilitation conditions, and innovation tendencies. However, it was found that the effect of performance expectations on acceptance intention was insignificant, with social impact having the greatest influence on acceptance intention and innovation tendency the least. Moderating effects of economic benefit and practical benefit between effort expectation and acceptance intention, moderating effect of practical benefit between social impact and acceptance intention, and moderating effect of economic benefit and practical benefit between facilitation condition and acceptance intention were found to be significant. On the other hand, it was found that economic benefits and practical benefits did not moderate the magnitude of the influence of performance expectations and innovation tendency on acceptance intention. These results suggest the following implications. First, in order to promote the use of big data by companies, the government needs to establish a policy to support the use of big data tailored to companies. Significant results can only be achieved when corporate members form a correct understanding and consensus on the use of big data. Second, it is necessary to establish and implement a platform specialized for agricultural data which can support standardized linkage between diverse agricultural big data, and support for a unified path for data access. Building such a platform will be able to advance the industry by forming an independent cooperative relationship between companies. Finally, the limitations of this study and follow-up tasks are presented.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.31-32
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• 2016

최근 정부의 R&D 연구에 대한 지원 규모 증가로 인해 전국가적으로 활발하게 기술 연구가 진행되고 있지만 예산을 집행하는 과정에서 기술 연구개발 과제의 중복연구로 시간과 예산을 낭비하는 사례를 노출하고 있다. 이와 같은 문제점을 해결하기 위해서는 정부 R&D 과제 선정과정에서 연구주제의 중복성 방지 등 근원적 혁신이 필요하다. 본 논문에서는 텍스트 마이닝 기술 및 빅데이터 분석 기술(하둡, 아마존 웹 서비스)과 같은 데이터 분석 기술이 도입된 클라우드 기반 R&D 연구 보고서 문서표절 및 유사도를 검출하는 시스템을 제안한다. 본 시스템은 SaaS 형태의 "on-demand software"로 웹 접속만으로 사용이 가능하다.

• Journal of Family Resource Management and Policy Review
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• v.27 no.1
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• pp.13-24
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• 2023

The 4th industrial revolution came deep into family life and changed the way of housework and care. The change in the family caused by the technological change of the 4th industrial revolution is remarkable in terms of socialization of housework. In this study, the socialization of housework, which is accelerating in the era of the 4th industrial revolution, was examined focusing on the change in the aspect of "household service" through the "platform". Since 2015, when technological changes in the 4th industrial revolution began to decline, related newspaper articles were extracted for daily and economic newspapers nationwide and analyzed big data. The results of big data analysis show that the platform economy using the 4th industrial revolution technology is rapidly spreading the socialization of housework not only at the business level but also at the public policy level. It has been confirmed that support for household services through the platform is growing into a new business area of companies, and at the public policy level, it is being treated as an important policy task in supporting work-family balance or responding to infectious diseases. This study is meaningful in that it provided an opportunity to reflect on the roles and tasks of the family, market, and state for housework and care in the future through changes in housework and care caused by the 4th industrial revolution technology.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.139-146
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• 2022

Recently, solving industrial and social problems and creating new values based on big data and AI is being discussed as the main policy goal. The new government also set the digital platform government as a national task in order to achieve new value creation based on big data and AI. However, studies that summarize and diagnose discussions over the past five years are insufficient. Therefore, this study diagnoses the discussions over the past 5 years using the 4th industrial revolution as a keyword. After collecting news editorials from 2017 to 2022 by applying the text mining technique, 9 major topics were discovered. In conclusion, this study provided implications for the government's task to prepare for the future society.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.102-112
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• 2020

Smart City's goal is to solve urban problems through smart city's component technology, thereby developing eco-friendly and sustainable economies and improving citizens' quality of life. Until now, smart cities have evolved into component technologies, but it is time to focus attention on the needs and participation of citizens in smart cities. In this paper, we present a big data procedure for solving smart city problems based on citizens' needs and participation. To this end, we examine the smart city project market by region and major industry. We also examine the development stages of the smart city market area by sector. Additionally it understands the definition and necessity of each sector for citizen participation, and proposes a method to solve the problem through big data in the seven-step big data problem solving process. The seven-step big data process for solving problems is a method of deriving tasks after analyzing structured and unstructured data in each sector of smart cities and deriving policy programs accordingly. To attract citizen participation in these procedures, the empathy stage of the design thinking methodology is used in the unstructured data collection process. Also, as a method of identifying citizens' needs to solve urban problems in smart cities, the problem definition stage of the design sinking methodology was incorporated into the unstructured data analysis process.

• KIISE Transactions on Computing Practices
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• v.22 no.6
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• pp.284-289
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• 2016

In the Hadoop-based Big Data analysis model, the data movement between the legacy system and the analysis system is difficult to avoid. To overcome this problem, a unified Big Data file system is introduced so that a unified platform can support the legacy service as well as the analysis service. However, major challenges in avoiding the performance degradation problem due to the interference of two services remain. In order to solve this problem, we first performed a real-life simulation and observed resource utilization, workload characteristics and I/O balanced level. Based on this analysis, two solutions were proposed both for the system level and for the technical level. In the system level, we divide I/O path into the legacy I/O path and the analysis I/O path. In the technical level, we introduce an aggressive prefetch method for analysis service which requires the sequential read. Also, we introduce experimental results that shows the outstanding performance gain comparing the previous system.

• The Journal of Bigdata
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• v.4 no.2
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• pp.175-183
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• 2019

Drop-out issue is one of the challenges of cyber university. There are about 130,000 students enrolled in cyber universities, but the dropout rate is also very high. To lower the dropout rate, cyber universities invest heavily in learning analytics. Some cyber universities analyze the possibility of dropout and actively support students who are more likely to drop out. The purpose of this paper is to identify the learning data affecting the dropout prediction index. As a result of the analysis, it is confirmed that number of lessons(progress), credits, achievement and leave of absence have a significant effect on dropout rate. It is necessary to increase the accuracy of the prediction model through post-test on the student dropout prediction index.