• The Journal of Asian Finance, Economics and Business
• /
• 제7권7호
• /
• pp.475-487
• /
• 2020

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 policy-making 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.

• International Journal of Contents
• /
• 제18권1호
• /
• pp.17-26
• /
• 2022

With the acceleration of urbanization and industrialization, air pollution has become increasingly serious, and the pollution control situation is not optimistic. Climate change has become a major global challenge faced by mankind. To actively respond to climate change, China has proposed carbon peak and carbon neutral goals. However, atmospheric pollutants and meteorological factors that affect air quality are complex and changeable, and the complex relationship and correlation between them must be further clarified. This paper uses China's 2013-2018 high-resolution air pollution reanalysis open data set, as well as statistical methods of the Pearson Correlation Coefficient (PCC) to calculate and visualize the design and analysis of environmental monitoring big data, which is intuitive and it quickly demonstrated the correlation between pollutants and meteorological factors in the temporal and spatial sequence, and provided convenience for environmental management departments to use air quality routine monitoring data to enable dynamic decision-making, and promote global climate governance. The experimental results show that, apart from ozone, which is negatively correlated, the other pollutants are positively correlated; meteorological factors have a greater impact on pollutants, temperature and pollutants are negatively correlated, air pressure is positively correlated, and the correlation between humidity is insignificant. The wind speed has a significant negative correlation with the six pollutants, which has a greater impact on the diffusion of pollutants.

• 디지털융복합연구
• /
• 제19권10호
• /
• pp.143-149
• /
• 2021

초연결 지능정보사회에 빠른 진입으로 빅데이터 기반의 자원관리 등을 위한 빅데이터시스템 구축의 필요성을 대두되면서, 공공기관에서 빅데이터시스템 구축을 추진하고 있는 실정이다. 이에, 본 연구는 공공기관 현실에 맞는 빅데이터시스템 구축 시 고려해야할 측정항목을 도출하고자 한다. 고등교육기관 통합정보시스템 구축의 환경요인 측정항목에 선행연구를 기반으로 빅데이터 관련연구들의 성공요인들과 공공기관 빅데이터 시스템 구축의 특성을 분석·결합하였다. 연구방법으로는 빅데이터 전문가들을 대상으로 델파이 방법등을 사용하여 빅데이터 특성이 반영된 19개 측정항목을 도출하였으며, 이를 빅데이터시스템에 구축하고자 하는 공공기관에 성공적으로 적용하기 위한 방안을 제언하였다. 본 연구결과가 공공기관에서 성공적인 빅데이터시스템 구축의 기초 자료로 활용되기를 기대한다.

• 한국환경보건학회지
• /
• 제47권6호
• /
• pp.505-512
• /
• 2021

Background: The occurrence of environmental disease is known to be associated with chronic exposure to toxic chemicals, including waterborne contaminants, air/indoor pollutants, asbestos, ingredients in humidifier disinfectants, etc. Objectives: In this study, we reviewed toxicological studies related to environmental disease as defined by the Environmental Health Act in Korea and toxic chemicals. We also suggested a direction for future toxicological research necessary for the prevention and management of environmental disease. Methods: Trends in previous studies related to environmental disease were investigated through PubMed and Web of Science. A detailed review was provided on toxicological studies related to the humidifier disinfectants. We identified adverse outcome pathways (AOPs) that can be linked to the induction of environmental diseases, and proposed a chemical screening system that uses AOP, chemical toxicity big data, and deep learning models to select chemicals that induce environmental disease. Results: Research on chemical toxicity is increasing every year, but there is a limitation to revealing a clear causal relationship between exposure to chemicals and the occurrence of environmental disease. It is necessary to develop various exposure- and effect-biomarkers related to disease occurrence and to conduct toxicokinetic studies. A novel chemical screening system that uses AOP and chemical toxicity big data could be useful for selecting chemicals that cause environmental diseases. Conclusions: From a toxicological point of view, developing AOP related to environmental diseases and a deep learning-based chemical screening system will contribute to the prevention of environmental diseases in advance.

• 창의정보문화연구
• /
• 제6권1호
• /
• pp.15-21
• /
• 2020

일반적인 산업현장에서의 빅 데이터 활용을 위해서는 먼저 산업현장의 설비, 공정, 환경 등에서 수집되는 정형화된 빅 데이터를 처리 및 저장하고, 비정형 데이터인 경우 비구조적 데이터로 저장하거나 정형 데이터로 변환하여 데이터베이스에 저장하여야 한다. 이러한 데이터베이스의 기본이 되는 데이터를 수집하기 위하여 본 논문에서는 산업현장의 계측정보, 환경 정보 등을 융합, 활용할 수 있는 개방형 IoT 표준기반의 빅데이터 수집 방안을 연구한다. 본 논문에서 제안된 빅 데이터 수집을 위한 플랫폼은 실시간 센싱 정보를 처리하기 위해 산업현장의 빅 데이터의 수집, 가공, 저장이 가능하며, 저장된 산업현장의 데이터를 활용 목적에 맞게 데이터를 처리 및 분석하는 다양한 빅 데이터 기술들을 적용할 수 있다.

• 한국환경보건학회지
• /
• 제47권5호
• /
• pp.462-471
• /
• 2021

Background: The number of synthesized chemicals has rapidly increased over the past decade. For many chemicals, there is a lack of information on toxicity. With the current movement toward reducing animal testing, the use of toxicity big data and deep learning could be a promising tool to screen potential toxicants. Objectives: This study identified potential chemicals related to reproductive and estrogen receptor (ER)-mediated toxicities for 1135 cleaning products and 886 laundry products. Methods: We listed chemicals contained in cleaning and laundry products from a publicly available database. Then, chemicals that potentially exhibited reproductive and ER-mediated toxicities were identified using the European Union Classification, Labeling and Packaging classification and ToxCast database, respectively. For chemicals absent from the ToxCast database, ER activity was predicted using deep learning models. Results: Among the 783 listed chemicals, there were 53 with potential reproductive toxicity and 310 with potential ER-mediated toxicity. Among the 473 chemicals not tested with ToxCast assays, deep learning models indicated that 42 chemicals exhibited ER-mediated toxicity. A total of 13 chemicals were identified as causing reproductive toxicity by reacting with the ER. Conclusions: We demonstrated a screening method to identify potential chemicals related to reproductive and ER-mediated toxicities utilizing chemical toxicity big data and deep learning. Integrating toxicity data from in vivo, in vitro, and deep learning models may contribute to screening chemicals in consumer products.

• 한국정보시스템학회지:정보시스템연구
• /
• 제32권2호
• /
• pp.137-152
• /
• 2023

Purpose The purpose of this study is to analyze big data related to DURIBAL from the operation of taxis reserved for the disabled to identify the issues and suggest solutions. ESG management should be translated into "environmental factors, social responsibilities, and transparent management." Therefore, the current study used Big Data analysis to analyze the factors affecting the standby of taxis reserved for the disabled and relevant problems for implications on convenience of social weak. Design/methodology/approach The analysis method used R, Excel, Power BI, QGIS, and SPSS. We proposed several suggestions included problems with managing cancellation data, minimization of dark data, needs to develop an integrated database for scattered data, and system upgrades for additional analysis. Findings The results showed that the total duration of standby was 34 minutes 29 seconds. The reasons for cancellation data were mostly use of other modes of transportation or delayed arrival. The study suggests development of an integrated database for scattered data. Finally, follow-up studies may discuss government-initiated big data analysis to comparatively analyze the use of taxis reserved for the disabled nationwide for new social value.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2015년도 학술발표회
• /
• pp.253-253
• /
• 2015

This research evaluated the effect of two main sources on the results of the ocean regional circulation model (ORCMs) during downscaling and nesting the results from the coarse data. The two sources should be the domain size, and temporal and spatial resolution different between driving and driven data. The Big-Brother Experiment is applied to examine the impact of them on the results of the ORCMs separately. Within resolution of 3km grid point ORCMs applying in the Big-Brother Experiment framework, it showed that the simulation results of the ORCMs depend on the domain size and specially the spatial and temporal resolution of lateral boundary conditions (LBCs). The domain size can be selected at 9.5 times larger than the interest area, and the spatial resolution between driving data and driven model can be up to 3 of ratio resolution and updating frequency of the LBCs can be up to every 6 hours per day.

• 한국빅데이터학회지
• /
• 제4권2호
• /
• pp.85-91
• /
• 2019

언제 어디서나 사용 가능한 스마트기기를 통한 온라인 쇼핑이 보편화되어 소비자들은 손쉽게 패션 관련 상품을 구입할 수 있다. 따라서 소비자들은 패션 관련 상품을 구매할 때 날씨, 판매 가격과 같은 다양한 환경 변수에 반응하여 상품을 구매한다. 따라서 효율적인 재고 관리를 위해 판매된 상품들의 빅데이터를 활용하는 것이 패션 산업에서 매우 중요하다. 본 논문에서는 국내 패션 회사 'A'의 실제 상품 판매 빅데이터를 활용하여 제안한 빅데이터 분석 알고리즘을 통해 기온 변화에 따른 패션 상품의 판매량 변화를 분석하였다. 분석 결과에 따르면, 제안한 빅데이터 분석 알고리즘을 통해 예상할 수 있는 판매량 결과와 예상하지 못한 판매량 결과를 얻었다.

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