• 인터넷정보학회논문지
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• 제17권3호
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• pp.115-128
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• 2016

Recently, converging Big Data and IoT(Internet of Things)has become mainstream, and public sector is no exception. In particular, this combinationis applicable to crime prevention in Korea. Crime prevention has evolved from CPTED (Crime Prevention through Environmental Design) to ubiquitous crime prevention;however, such a physical engineering method has the limitation, for instance, unexpected exposureby CCTV installed on the street, and doesn't have the function that automatically alarms passengers who pass through a criminal zone.To overcome that, this paper offers a crime prevention method using Big Data from public organizations along with IoT. We expect this work will help construct an intelligent crime-prevention system to protect the weak in our society.

• 디지털융복합연구
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• 제15권4호
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• pp.13-25
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• 2017

데이터의 저장량이 이전 보다 더욱 증가하고 있는데, 이는 정부와 기업은 물론이고 일반 사용자의 적극적인 참여에 기인하고 있다. 이러한 빅데이터 시대에는 정부 및 공공기관의 역할이 중요하게 부각되는데, 이들은 공공의 목적을 이유로 일반 개인의 정보에도 접근하여 그것을 다룰 수 있기 때문이다. 이에 본 연구에서는 빅데이터에 대한 현황과 대응방안을 국가별로 조사하여 몇 가지 시사점을 도출할 수 있었다. 연구결과 먼저 빅데이터와 관련된 인력 및 기술의 확보가 우선되어야 한다는 점을 들 수 있었다. 또한 정부와 민간 사이의 적극적인 공유와 개발노력이 동반되어야 한다는 것도 알 수 있었다. 그리고 데이터의 적재와 분석방법이 계속 발전되는 만큼 장기적인 전략 수립을 마련해야 한다는 점도 확인하였다. 결론적으로 빅데이터의 정책적 활용을 위해서는 데이터 관리의 중요성을 재인식하고, 개인정보 보호에 주력하여야 하며, 현실적용 능력을 배가시켜야 한다는 것을 시사점으로 제안하고자 한다.

• International Journal of Advanced Culture Technology
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• 제9권3호
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• pp.174-180
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• 2021

The necessity of this study is as follows. A decrease in the number of newborns, an increase in the youth unemployment rate, and a decrease in the employment rate are having a fatal impact on universities. To help increase the employment rate of universities, we intend to utilize Big Data of university public information. Big data refers to the process of collecting and analyzing data, and includes all business processes of finding data, reprocessing information in an easy-to-understand manner, and selling information to people and institutions. Big data technology can be divided into technologies for storing, refining, analyzing, and predicting big data. The purpose of this study is to find the vision and special department of a university with a high employment rate by using big data technology. As a result of the study, big data was collected from 227 universities on www.academyinfo.go.kr site, We selected 130 meaningful universities and selected 25 universities with high employment rates and 25 universities with low employment rates. In conclusion, the university with a high employment rate can first be said to have a student-centered vision and university specialization. The reason is that, for universities with a high employment rate, the vision was to foster talents and specialize, whereas for universities with a low employment rate, regional bases took precedence. Second, universities with a high employment rate have a high interest in specialized departments. This is because, as a result of checking the presence or absence of a characterization plan, universities with a high employment rate were twice as high (21/7). Third, universities with high employment rates promote social needs and characterization. This is because the characteristic departments of universities with high employment rates are in the order of future technology and nursing and health, while universities with low employment rates promoted school-centered specialization in future technology and culture, tourism and art. In summary, universities with high employment rates showed high interest in student-centered vision and development of special departments for social needs.

• Journal of Preventive Medicine and Public Health
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• 제56권1호
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• pp.59-66
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• 2023

Objectives: The aim of this study was to evaluate the effect of body weight status and sleep duration on the discrete-time hazard of menarche in Korean schoolgirls using multiple-point prospective panel data. Methods: The study included 914 girls in the 2010 Korean Children and Youth Panel Study who were in the elementary first-grader panel from 2010 until 2016. We used a Gompertz regression model to estimate the effects of weight status based on age-specific and sex-specific body mass index (BMI) percentile and sleep duration on an early schoolchild's conditional probability of menarche during a given time interval using general health condition and annual household income as covariates. Results: Gompertz regression of time to menarche data collected from the Korean Children and Youth Panel Study 2010 suggested that being overweight or sleeping less than the recommended duration was related to an increased hazard of menarche compared to being average weight and sleeping 9 hours to 11 hours, by 1.63 times and 1.38 times, respectively, while other covariates were fixed. In contrast, being underweight was associated with a 66% lower discrete-time hazard of menarche. Conclusions: Weight status based on BMI percentiles and sleep duration in the early school years affect the hazard of menarche.

• 한국멀티미디어학회논문지
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• 제20권11호
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• pp.1785-1792
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• 2017

Generally, big data contains sensitive information about individuals, and thus directly releasing it for public use may violate existing privacy requirements. Therefore, privacy-preserving data publishing (PPDP) has been actively researched to share big data containing personal information for public use, while protecting the privacy of individuals with minimal data modification. Recently, with increasing demand for big data sharing in various area, there is also a growing interest in the development of software which supports a privacy-preserving data publishing. Thus, in this paper, we develops the system which aims to effectively and efficiently support privacy-preserving data publishing. In particular, the system developed in this paper enables data owners to select the appropriate anonymization level by providing them the information loss matrix. Furthermore, the developed system is able to achieve a high performance in data anonymization by using distributed Hadoop clusters.

• 한국콘텐츠학회논문지
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• 제15권2호
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• pp.377-392
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• 2015

최근 들어 빅 데이터의 영향력이 증가하면서 데이터 중심의 사회로 급변하고 있다. 동시에 데이터의 수집, 관리 및 활용의 문제가 중요한 이슈로 대두되고 있다. 더욱이 빅 데이터는 가공과 분석에 따라 상황인식, 의사결정, 그리고 미래 예측을 가능하게 하는 영역까지 확대되고 있다. 심지어 재난관리에 있어서도 민간 및 공공 영역에서 만들어지는 엄청난 양의 정형 및 비정형 데이터들을 의미 있는 정보로 가공해내는 것이 무엇보다 중요하다. 실효성 있는 재난관리를 위해 공공과 민간 데이터가 동시에 연계 분석되어야 한다는 것이다. 그래서 본 연구에서는 문헌조사와 사례 연구 통해 국가 재난관리를 위한 효율적인 빅 데이터 활용 정책방안을 제안하고자 하였다. 연구 결과 국내 재난관리의 빅 데이터 활용 촉진 방안에 대한 공공 및 민간 부문의 역할을 각각 도출하였다. 공공과 민간 영역에서 공통적으로 추진해야 할 정책과제는 재난관련 빅 데이터의 공개 및 공유, 기술 및 인프라의 확충, 법 및 제도적 정비, 소셜 네트워크 서비스를 활용한 재난 정보전달 시스템의 구축, 빅데이터 전문 인력의 양성으로 나타났다.

• International Journal of Advanced Culture Technology
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• 제8권3호
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• pp.133-139
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• 2020

The purpose of this study is to contribute to the provision of basic data for psychological quarantine policy and counseling by examining the public perception of the "corona blue" phenomenon through analysis of social media big data. To do this, key words related to the word 'Corona Blue' were derived and analyzed using the big data analysis program 'Textom'. As a result of the analysis, words such as 'Corona 19', 'depression', 'problem' and 'overcome' were derived as key words. For the analysis results,"pride and awarenes as the public perception of Corona 19", "depression and anxiety as a group trauma as the corona blue phenomenon", "spreading a psychological quarantine culture and demanding social healing as the perception of overcoming corona Blue," and "hope for return to daily life and changes in daily life as the perception of post corona" were discussed. In conclusion, we have identified the need for active psychological support from the community By revealing that Corona Blue is a depression as a group trauma. At this time, it is confirmed that it is necessary to prioritize social healing and psychological quarantine for the main risk groups such as youth or the vulnerable, who are the socially weak.

• 디지털산업정보학회논문지
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• 제14권4호
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• pp.57-68
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• 2018

This paper presents a smart firefighting appliances monitoring system based on big data analytics platform using GS1 for Smart City. Typical firefighting appliances are fire hydrant, fire extinguisher, fire alarm, sprinkler, fire engine, etc. for the fire of classes A/B/C/D/E. Among them, the dry chemical fire extinguisher have been widely supplied and 6 millions ones were replaced for the aging ones over 10 years in the past year. However, only 5% of them have been collected for recycling of chemical materials included the heavy metals of environment pollution. Therefore, we considered the trace of firefighting appliances from production to disposal for the public open service. In the paper, we suggest 1) a smart firefighting appliances system using GS1, 2) a big data analytics platform and 3) a public open service and visualization with the analyzed information, for fire extinguishers from production to disposal. It can give the information and the visualized diagrams with the analyzed data through the public open service and the free Apps.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.17-24
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• 2018

Today, we are living in the era of data and information. With the advent of Internet of Things (IoT), the popularity of social networking sites, and the development of mobile devices, a large amount of data is being produced in diverse areas. The collection of such data generated in various area is called big data. As the importance of big data grows, there has been a growing need to share big data containing information regarding an individual entity. As big data contains sensitive information about individuals, directly releasing it for public use may violate existing privacy requirements. Thus, privacy-preserving data publishing (PPDP) has been actively studied to share big data containing personal information for public use, while preserving the privacy of the individual. K-anonymity, which is the most popular method in the area of PPDP, transforms each record in a table such that at least k records have the same values for the given quasi-identifier attributes, and thus each record is indistinguishable from other records in the same class. As the size of big data continuously getting larger, there is a growing demand for the method which can efficiently anonymize vast amount of dta. Thus, in this paper, we develop an efficient k-anonymity method by using Spark distributed framework. Experimental results show that, through the developed method, significant gains in processing time can be achieved.

• 지역연구
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• 제36권1호
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• pp.51-67
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• 2020

본 연구는 건축물대장, 주민등록대장 등 공공빅데이터의 활용성을 탐구하기 위해, 비교적 간단한 구조를 가진 맨큐-와일(MW)모형을 활용해 1인당 주거면적 추정을 시도하였다. 그 결과 공공빅데이터를 활용하여 정기조사 방식에 버금가는 모형을 수립할 수 있고, 기존 정기조사 방식으로는 어려웠던 기초자치단체별 모형수립도 가능함을 확인할 수 있었다. 공공빅데이터로부터 일반단독주택과 다세대주택 샘플을 판별하는 과정을 설계하여, 10세 연령대별 1인당 주거면적을 추정하고, 인구주택총조사, 주거실태조사 등 기존 정기조사 자료를 활용한 결과와 비교해 일치시킨 후, 서울시 25개 자치구별 1인당 주거면적을 도출하였다. 공공빅데이터는 지식영역을 확장시켜주는 장점이 있지만 본래의 작성의도와 다른 목적으로 생성된 자료를 활용한다는 점에서 근본적 한계는 존재한다. 또 개인정보 접근이라는 어려운 과정은 분석을 보다 신중히 진행해야 하는 부담을 주고, 비식별화를 거친 자료를 분석함에 따라 연구설계가 어려워지는 문제도 있다. 향후 공공빅데이터가 기존 통계조사를 보완하거나 대체할 수도 있도록 가공하는 방법 등에 대한 꾸준한 연구가 필요할 것으로 보인다.