As The Fourth Industrial Revolution and Intelligent Information Age came into full-scale, the policy of open government data has become a hot topic for each country. The United States, the United Kingdom, and other countries are shifting policy direction to “creating value” of open government data. Also, in the age of the digital economy where the data market is soaring, open government data is gradually being recognized as a new raw material for new business and start-ups. In addition, Korea ranked first in the OECD open government data evaluation twice in a row, and was highly evaluated in the international evaluation. However, domestic firms are still lacking in qualitative openness of government data, data is dispersed among institutions, lack of public-private data linkage, and development of app-oriented development. This study attempts to analyze major national policies for the creation of a data ecosystem that considers data lifecycle, from production to storage, distribution and utilization of data. First, the target countries were the leading public data countries among the OGP member countries, the USA, the UK, Australia and Canada. The results of this study are as follows. As a result of analyzing the results and comparing Korea’s policies, it was concluded that most of Korea is superior in open government data policy. However, improvement of data quality, development of open data portal as an open platform, support for finding various users including apps and web development companies, and cultivation of open government data utilizing personnel are analyzed as policy issues. In addition, the direction of policy for the balanced ecosystem of Korea is presented together.

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

This paper presents the novel observation model, called Modified Spherical Signature Descriptor(MSSD), capable of representing 2D image generated from 3D point cloud data. The Modified Spherical Signature Descriptor has a uniform mesh grid to accumulate the occupancy evidence caused by neighbor point cloud data. According to a kind of area such as wall, road, tree, car, and so on, the evidence pattern of 2D image looks so different each other. For the parameter learning of Convolutional Neural Network(CNN) layers, these 2D images were applied as the input layer. The Convolutional Neural Network, one of the deep learning methods and familiar with the image analysis, was utilized for the urban structure classification. The case study on CNN practice was introduced in detail in this paper. The simulation results shows that the classification accuracy of CNN with 2D images of the proposed MSSD was improved more than the traditional methods' one.

As the 3D laser scanning technology capable of databaseing large sewage box culverts becomes possible, it is necessary to develop a standardization manual that can clearly distinguish the structural and operational defect types of box culver and analyze the defect data. In this study, we collected and analyzed defects in sewage box culverts of 14,827m in total by selecting three districts in Korea. The major defects were surface damages, and their defect densities were 2.17 m2/m, 0.27 m2/m and 0.10 m2/m for aggregate exposure, Steel reinforcement exposure, and Steel reinforcement projecting. In order to support the decision of the box culverment management, it was divided into five grades and each defect code and defect score were allocated. The results of this study are useful for the diagnosis of the sewage box culverts in Korea and it is expected to support a decision making for management.

토목분야 생산성 향상을 위해 BIM을 적용하기 위한 노력이 계속되고 있으나, 선형과 지반에 대한 정보가 필수적인 터널 구조물의 정보모델링에 대한 연구는 미흡한 상황이다. AMT에서 생성된 선형의 이산화를 통해 포인트의 정보를 BAT로 전 달하여 곡선 선형을 반영한 터널 모델 생성 방안을 제시하였다. 철도 구조물과 선형에 대한 물리적 요소와 공간적 요소를 모두 고려할 수 있도록 IFC 데이터 스키마를 확장하였으며, 확장된 데이터 스키마를 참조하여 선형, 구조물, 지반 정보에 대한 의미정보를 PSET에 담아 IFC기반의 정보관리를 가능하게 하였다. 제안한 방법에 따라 생성한 정보모델을 통해 터널과 밀접한 암반 등급을 자동으로 도출함으로써 활용성을 검증하였다.

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity , ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

Our research is aimed at predicting recent trend and leading technology for the future and providing optimal Nano technology trend information by analyzing Nano technology trend. Under recent global market situation, Users’ needs and the technology to meet these needs are changing in real time. At this point, Nano technology also needs measures to reduce cost and enhance efficiency in order not to fall behind the times. Therefore, research like trend analysis which uses search data to satisfy both aspects is required. This research consists of four steps. We collect data and select keywords in step 1, detect trends based on frequency and create visualization in step 2, and perform analysis using data mining in step 3. This research can be used to look for changes of trend from three perspectives. This research conducted analysis on changes of trend in terms of major classification, Nano technology of 30’s, and key words which consist of relevant Nano technology. Second, it is possible to provide real-time information. Trend analysis using search data can provide information depending on the continuously changing market situation due to the real-time information which search data includes. Third, through comparative analysis it is possible to establish a useful corporate policy and strategy by apprehending the trend of the United States which has relatively advanced Nano technology. Therefore, trend analysis using search data like this research can suggest proper direction of policy which respond to market change in a real time, can be used as reference material, and can help reduce cost.

Data clustering is one of the most difficult and challenging problems and can be formally considered as a particular kind of NP-hard grouping problems. The K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, it has high possibility to trap in local optimum and high variation of solutions with different initials for the large data set. Therefore, we need study efficient computational intelligence method to find the global optimal solution in data clustering problem within limited computational time. The objective of this paper is to propose a combined artificial bee colony (CABC) with K-means for initialization and finalization to find optimal solution that is effective on data clustering optimization problem. The artificial bee colony (ABC) is an algorithm motivated by the intelligent behavior exhibited by honeybees when searching for food. The performance of ABC is better than or similar to other population-based algorithms with the added advantage of employing fewer control parameters. Our proposed CABC method is able to provide near optimal solution within reasonable time to balance the converged and diversified searches. In this paper, the experiment and analysis of clustering problems demonstrate that CABC is a competitive approach comparing to previous partitioning approaches in satisfactory results with respect to solution quality. We validate the performance of CABC using Iris, Wine, Glass, Vowel, and Cloud UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KABCK (K-means+ABC+K-means) is better than ABCK (ABC+K-means), KABC (K-means+ABC), ABC, and K-means in our simulations.

The mortality rate in industrial accidents in South Korea was 11 per 100,000 workers in 2015. It’s five times higher than the OECD average. Economic losses due to industrial accidents continue to grow, reaching 19 trillion won much more than natural disaster losses equivalent to 1.1 trillion won. It requires fundamental changes according to industrial safety management. In this study, We classified the risk of accidents in industrial complex of Ulju-gun using spatial analytics and data mining. We collected 119 data on accident data, factory characteristics data, company information such as sales amount, capital stock, building information, weather information, official land price, etc. Through the pre-processing and data convergence process, the analysis dataset was constructed. Then we conducted geographically weighted regression with spatial factors affecting fire incidents and calculated the risk of fire accidents with analytical model for combining Boosting and CART (Classification and Regression Tree). We drew the main factors that affect the fire accident. The drawn main factors are deterioration of buildings, capital stock, employee number, officially assessed land price and height of building. Finally the predicted accident rates were divided into four class (risk category-alert, hazard, caution, and attention) with Jenks Natural Breaks Classification. It is divided by seeking to minimize each class’s average deviation from the class mean, while maximizing each class’s deviation from the means of the other groups. As the analysis results were also visualized on maps, the danger zone can be intuitively checked. It is judged to be available in different policy decisions for different types, such as those used by different types of risk ratings.

최근 머신러닝은 빅데이터에 대한 분석방법으로서 학습을 통한 지능화된 문제해결 방안으로서 관심이 증가하고 있다. 본 논문은 LBSN 데이터와 머신러닝 방식을 이용하여 토지이용현황을 파악하는 분석을 시도하였다. 도시계획에 있어서 토지이용현황의 파악은 직접적인 현장 조사에 의존해 왔다. 최근 스마트폰 사용자가 증가하면서 등장하고 있는 위치기반 소셜미디어의 자료들 은 토지이용의 상황을 반영하는 빅데이터로서, 머신러닝 방법론은 이들에 대한 자동화된 분석을 할 수 있게 한다. 본 연구에서는 LBSN 자료와 머신러닝 기법을 이용하여 토지이용을 예측하는 모델을 개발하여 실제 토지이용현황 자료와의 비교분석을 수행하였다. 이러한 분석을 통해 LBSN자료를 이용한 토지이용현황의 자동화된 분석 방안에 대해 연구하였다.

목적 : 본 연구는 상대가치점수를 기반으로 하는 국내 건강보험수가의 행위별 수가제가 국제 기능･장애 및 건강에 대한 분류(International Classification of Functioning, Disability and Health; ICF)의 건강 개 념에 적합한 비용을 지출하고 있는지 알아보고자 하였다. 연구방법 : 2003년-2013년 건강보험 및 의료급여권자 중 인구전체를 대표하는 100만 명의 샘플인 국민 건강보험공단의 건강보험 표본코호트 자료를 이용하였다. 건강보험요양급여비용의 이학요법료 중 제3절 전문재활치료료에 해당하는 행위들을 건강보험심사평가원에서 제시한 행위정의에 따라 신체기능과 활동 및 참여로 분류한 후 청구 통계량을 비교분석하였다. 결과 : 국내 재활치료 수가체계는 독립적인 일상생활활동, 활동/참여 그리고 가정이나 사회로 복귀를 통한 삶의 질 향상이라는 ICF의 건강 및 재활의학의 개념을 반영하지 못하고 있다. 또한, 환자의 상병군, 중 증도에 따른 재활치료의 효율적 수행을 위한 급성기–아급성기(회복기)-만성기의 재활의료체계가 정립되어 있지 않음을 확인하였다. 결론 : 재활치료의 효율적 수행을 위해서는 급성기- 아급성기(회복기)- 만성기의 재활의료체계가 정립되어야 하고 재활의료체계 내에서 의료기관 종별 역할이 구분이 필요하다. 이와 함께 적절한 재활치료 보험수가 체계 그리고 심사기준의 신설 및 개선이 필요하다.

전 세계적으로 금융선진국을 비롯한 각 국가의 금융당국은 금융기관과 금융소 비자 간의 정보비대칭 완화 및 이를 통한 리스크관리를 위하여 금융기관이 참여하는 신용정 보 공유제도를 운영하고 있다. 본 연구는 한국에서 공유되고 있는 신용정보 중 사고정보를 대상으로 하여 실제로 공유중 인 신용정보 데이터를 분석하였다. 사고정보를 사고횟수, 사고기간, 사고금액의 세 종류로 구 분하여, 생존분석에서는 사고정보가 기업의 생존기간에 미치는 영향을 분석하였고, 이후 집 단 간 비교분석을 통해 업력 7년 이하의 창업기업과 그 외 기존기업 간에 존재하는 사고정보 양상 차이를 검증하였다. 총 449,579개 기업의 사고정보에 대한 정량적인 분석을 시행한 결과 생존분석에서 사고횟 수가 사고후생존기간과 정(+)의 상관관계를 보였는데 이는 사고횟수를 부정적인 요소로 판단하고 있는 금융기관의 현행 리스크정책에 반증적 성격을 갖는다. 또한, 집단 간 비교분석 에서는 창업기업의 사고양상이 기존기업보다 생존기간에 더 긍정적인 모습을 보이고 있음에 따라 창업기업의 특성을 고려한 신용정보 공유제도의 개선이 필요하다는 시사점을 도출할 수 있었다.

지금까지의 노인자살 연구들은 대부분이 노인자살에 관한 이론적 조망과 그에 따른 노인자살 요인들을 파악하는 데 집중되어 왔다. 그래서 노인자살의 예방대책에 관한 논의는 개괄적이고 제안적인 수준에 머 무르고 있다. 현재 우리나라는 자살사고를 효과적으로 예방하기 위한 자살예방사업 및 인프라가 극히 미비한 상태이 며 특히 노인자살의 특수성이 정책에 반영되지 못하고 있다. 우리사회에서 사회적 이슈가 되고 있는 노인 자살문제에 대해 효과적이고 실효성 있는 정책과 대안을 제시하기 위해서는 노인자살에 대한 정확한 특성 과 실태에 대한 분석이 필요하다. 이에 본 연구자는 통계청에서 제시하고 있는 마이크로데이터를 활용해 MDIS(MicroData Integrated Service)에서 2017년 10월 31일 공개된 통계 기초자료(Microdata)인 인구동향조사 「2016년 사망(원인) 통계」마이크로데이터를 수집하여 우리나라 자살에 대한 특성 및 실 태를 역학적으로 분석하였으며, 실천적이고 정책적인 제언을 제시하였다. 분석방법은 자살통계 데이터를 중심으로 첫째, 자살자의 인구사회학적 특성, 및 자살특성을 파악하기 위해 빈도분석 및 기술통계분석을 실시하였고. 둘째, 인구사회학적 특성에 따른 자살특성 차이를 알아보기 위해 교차분석을 실시하였다.