• 스마트미디어저널
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• 제11권10호
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• pp.54-64
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• 2022

빅데이터 기반의 디지털 트랜스포메이션은 데이터 및 데이터 관련 기술을 통해 기업의 성과 향상, 조직 변화, 사회 공헌 등의 목적 달성을 위해 수행하는 혁신적 프로세스를 의미한다. 성공적인 빅데이터 기반의 디지털 트랜스포메이션을 위해서는 관련 연구 현황, 주요 연구토픽, 주요 연구토픽 간의 관계를 이해하는 것이 필수적이다. 그러나 여러 연구들의 서로 다른 관점 및 이들 간 연계 가능성에 대해 이해하려는 노력은 아직 미진하다. 본 논문은 텍스트마이닝을 활용하여 관련 연구동향을 분석하고, 여러 연구의 다양한 관점을 통합적으로 이해하기 위한 기반 마련을 시도해보았다. Web of Science Core Collection에서 추출한 439편의 논문을 분석하여, 10개의 주요 연구토픽을 도출하였고, 이들 간의 관계를 분석하였다. 본 연구의 결과가 빅데이터 기반의 디지털 트랜스포메이션에 대한 통합적인 이해를 촉진하고, 성공을 위한 방향성 모색에 기여할 것으로 기대한다.

• 한국콘텐츠학회논문지
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• 제21권10호
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• pp.103-114
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• 2021

본 연구는 데이터 과학의 분석적 연구 방법을 디자인 연구에 활용할 수 있는 방법을 모색, 실증해봄으로써 빅데이터 기반의 디자인 분야에서 데이터 자원을 효과적으로 활용하고, 빅데이터 기술과 디자인이 상생 발전할 수 있는 디자인 연구 방향을 제시하였다. 본 연구의 방법은 먼저 고전적인 디자인 리서치의 방법들과 데이터를 활용한 디자인 리서치의 연구 방법론, 데이터 과학의 연구 방법론을 고찰하고 디자인과 데이터 과학의 선행연구들을 종합하여 데이터 과학의 연구 방법론과 디자인 프로세스를 통합하는 데이터 기반 디자인의 연구 방법론을 개발 제안하였다. 제안된 연구 모델은 데이터 기반 서비스 앱의 디자인 리서치 사례 연구를 수행하여 제안한 방법론의 적용 가능성을 살펴보았으며, 제시한 연구 방법의 적용을 위하여 데이터 큐레이션 방법에 대한 연구와 디자이너의 데이터 문해력 확보가 향후 연구과제로 도출되었다.

• Composites Research
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• 제36권5호
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• pp.329-334
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• 2023

기존의 멀티스케일 유한요소법(Multiscale finite element, FE² )은 거시 스케일의 모든 적분점에서 대표 체적요소(representative volume element, RVE)의 미시 경계치 문제를 반복적으로 계산하기 때문에 긴 해석 시간과 많은 데이터 저장 공간을 필요로 한다. 이를 해결하기 위해 본 연구에서 평균장 균질화 데이터 기반 멀티스케일 해석 기법을 개발하였다. 데이터 기반 전산역학(data-driven computational mechanics, DDCM) 해석은 변형률-응력 데이터 셋을 직접적으로 사용하는 모델-프리(model-free)접근 방식이다. 멀티스케일 해석을 수행하기 위해, 평균장 균질화(mean-field homogenization)를 활용하여 복합재의 미세구조에 대한 변형률-응력 데이터베이스(database)를 효율적으로 구축하고, 이를 기반으로 데이터 기반 전산역학 시뮬레이션을 수행하였다. 본 논문에서는 개발한 멀티 스케일 해석 프레임워크(framework)를 예제에 적용하여, 초탄성(hyperelasticity) 복합재의 미세 구조를 고려한 데이터 기반 전산역학 시뮬레이션 결과를 확인하였다. 따라서, 데이터 기반 전산역학 접근 방식을 활용한 멀티스케일 해석기법은 다양한 재료 및 구조에 적용될 수 있으며, 멀티스케일 해석 연구 및 응용 가능성을 열어줄 것으로 기대된다.

• 아시아태평양코퍼스연구
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• 제2권2호
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• pp.43-48
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• 2021

The aim of this study is to analyze the trends of corpus driven research in Korean education. For this purpose, a total of 14 papers was searched online with the keywords including Korean corpus and Korean education. The data was categorized into three: vocabulary education, grammar education and corpus data construction methods. The analysis results suggest that the number of corpus studies in the field of Korean education is not large enough but continues to increase, especially in the research on data construction tools. This suggests there is a significant demand in corpus driven studies in Korean education field.

• 지식경영연구
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• 제17권1호
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• pp.49-72
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• 2016

Extant research on firm innovation productivity is limited in measuring the innovation productivity, in which they measured firm innovation productivity by using either inputs or outputs of innovation. The present study complemented the extant research by employing Data Envelopment Analysis (DEA) approach to measure firm innovation productivity. Furthermore, this paper examined the effects of firms' external knowledge search, as one of open innovation practices, on firm innovation productivity, for open innovation activities are regarded as an influencing factor on firm innovation productivity in the previous literatures. Using the data of the Korean Innovation Survey (KIS) of manufacturing industries conducted in 2008, this study developed hypotheses in which we considered not only two dimensions of external knowledge search (breadth and depth) but also two subtypes of external knowledge search (market-driven and science-driven). The results found that searching deeply and market-driven search are positively related to firm innovation productivity, but science-driven search is somewhat negatively related to firm innovation productivity. Furthermore, market-driven search can mitigate the negative effect of science-driven search on innovation productivity.

• 간호행정학회지
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• 제21권3호
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• pp.308-316
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• 2015

Purpose: The purpose of this study was to identify the level of project perception for those nurses from research-driven hospitals and to analyze the effect of research-relevant performance in the health care field focusing on the mediated effect of research capacity and job satisfaction. Methods: Data were collected from June, 2014 to July, 2014, and participants were 106 research nurses in Research-driven hospitals. Descriptive statistics, Independent t-test, One-way ANOVA, structural equation modeling (SEM). Results: As a result, Research-relevant performance according to project perception of research nurses from Research-driven Hospitals was not statistically significant, but research capacity and job satisfaction had a mediating role. Evaluation System Perception was significantly different from Research Capacity (p<.001), Research Capacity was significantly different from Job Satisfaction (p<.001), Job Satisfaction was significantly different from Research Performance (p<.001) Conclusion: The results indicate that research capacity building and job security research nurses are able to contribute to improving research performance of research-driven hospitals.

• 아태비즈니스연구
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• 제15권1호
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• pp.131-143
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• 2024

Purpose - The purpose of this study was to analyze cases of big data-driven business in the financial industry, focusing on organizational structure and business processes using big data in banking industry. Design/methodology/approach - This study used a case study approach. To this end, cases of two banks implementing big data-driven business were collected and analyzed. Findings - There are two things in common between the two cases. One is that the central tasks for big data-driven business are performed by a centralized organization. The other is that the role distribution and work collaboration between the headquarters and business departments are well established. On the other hand, there are two differences between the two banks. One marketing campaign is led by the headquarters and the other marketing campaign is led by the business departments. The two banks differ in how they carry out marketing campaigns and how they carry out big data-related tasks. Research implications or Originality - When banks plan and implement big data-driven business, the common aspects of the two banks analyzed through this case study can be fully referenced when creating an organization and process. In addition, it will be necessary to create an organizational structure and work process that best fit the special situation considering the company's environment or capabilities.

• KIEAE Journal
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• 제17권5호
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• pp.77-86
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• 2017

Purpose: Because of the growing concern over fossil fuel use and increasing demand for greenhouse gas emission reduction since the 1990s, the building energy analysis field has produced various types of methods, which are being applied more often and broadly than ever. A lot of research products have been actively proposed in the area of the building energy simulation for over 50 years around the world. However, in the last 20 years, there have been only a few research cases where the trend of building energy analysis is examined, estimated or compared. This research aims to investigate a trend of the building energy analysis by focusing on methodology and characteristics of each method. Method: The research papers addressing the building energy analysis are classified into two types of method: engineering analysis and algorithm estimation. Especially, EPG(Energy Performance Gap), which is the limit both for the existing engineering method and the single algorithm-based estimation method, results from comparing data of two different levels- in other words, real time data and simulation data. Result: When one or more ensemble algorithms are used, more accurate estimations of energy consumption and performance are produced, and thereby improving the problem of energy performance gap.

• 인터넷정보학회논문지
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• 제19권1호
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• pp.123-130
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• 2018

With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

• 아시아태평양코퍼스연구
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• 제4권1호
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• pp.25-42
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• 2023

The number of studies on data-driven learning (DDL) has increased in recent years, and DDL's overall effectiveness as an L2 (second language) teaching methodology has been reported to be high. However, the degree of its effectiveness in grammar instruction, particularly for the goal of correcting errors in L2 writing, is still unclear. To provide guidelines for focused grammar instruction with DDL in the Japanese classroom setting, we aimed to identify the typical grammatical errors made by Japanese learners in the Cambridge Learner Corpus First Certificate in English (CLC FCE) dataset. The results revealed that three error types (nouns, articles, and prepositions) should be addressed in DDL grammar instruction for Japanese English as a foreign language (EFL) learners. In light of the findings, pedagogical implications and suggestions for future DDL research and practice are discussed.