• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.251-262
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• 2010

While outsourcing has become a basic strategy of the information system adoption, there is an emerging needs to analyze the gap between the required data and the existing data for the new system from an adopting company's perspective. In CRM adoption failure cases, the first reason is adopting company pay no attention to the data that will support investment and systems. So far, there is no attempt to consider data driven approach in information system adoption field. Hence, we propose Information System Adoption Model based on Data (ISAMD) and show how to use in real world by simulation. By using ISAMD, information system adoption decision maker can simulate the needed data and related cost with various information system alternatives in short term, and long term planning. ISAMD can prevent the possible threat of unexpected data cost in adopting new system at the adopting decision stage.

• Asian Journal of Innovation and Policy
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• v.2 no.2
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• pp.240-264
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• 2013

The study compares network structures that emerged in three inter-organisational projects set up under the MSC Malaysia initiative by the Government of Malaysia. These consortia are seen as policy-driven inter-organisational networks and, with data collected through interviews; the links among the organisations are mapped to gain an understanding of the structures that emerged in these networks. The findings provide lessons for other emerging countries that are embarking on similar projects i.e. cluster-oriented developments with policy-driven inter-organisational networks. These findings are seen as particularly useful when emerging countries invest in technology-related projects and invite multinational companies to work together with local firms.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.11a
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• pp.65-70
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• 2005

Recently, Grid is embossed as a new issue according to the need of cooperation related to distributed resources, data sharing, Interaction and so on. It focuses on sharing of large scale resources, high-performance, applications of new paradigms, which improved more than established distributed computing. Because of the environmental specificity distributed geographically and dynamic, the most important problem in grid environment is to share and to allocate distributed grid resources. This paper proposes supply-driven strategies model that is applicable for resource management in grid environment and presents a optimal resource allocation algorithm based on resource demands. Supply-driven strategies model can offer efficient resource management by transaction allocation based on user demand and provider strategy. This paper implements the supply-driven strategies model on the DEVS modeling and simulation environment and shows the efficiency and excellency of this model by comparing with established models.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.608-610
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• 2023

의료 분야에서 AI 모델의 활용이 증가하고 있지만, 모델의 예측 불확실성을 정확하게 평가하고 표현하는 것이 중요하다. 본 연구는 이러한 문제를 해결하기 위해 AI-driven 방식을 제안하며, 특히 의료 영상 변환 모델에 대한 불확실성 표현과 데이터 한계 극복 방법론을 제안한다. 제안된 AI-driven 안저영상 변환 모델은 기존 GAN과는 다르게 구조가 이루어져 있으며, 신뢰도가 낮은 영역을 구분하고 시각화하여 표현할 수 있다. 실험 결과, 제안된 방법은 기존 모델과 비교하여 영상 변환 성능이 크게 향상되었으며, 불확실성에 대한 정확도 평가에서도 AI-driven 방식이 높은 성능을 보인다. 결론적으로, 본 연구는 AI-driven 방식을 통해 의료 AI에서의 불확실성 표현의 가능성을 확인하였으며, 이 방식이 데이터의 한계와 불확실성을 극복할 수 있을 것으로 기대된다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.149-165
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• 2016

The objectives of this study is to assess the influence of individual characteristics and economic environment on the entrepreneurship such as entrepreneurial Intention and behavior based on the theory of Planned behaviors. This study used a country-level merged data set composed of GEM(Global Entrepreneurship Monitor) data and the OECD Statistics data. And this used the fixed effect model to analyze the panel data of 31 OECD countries during the period from 2005 to 2014. Our findings show that subjective norm has a significantly positive effect on entrepreneurial intention. In individual characteristics, the perceived opportunities has a significantly positive effect on early-stage entrepreneurial activity(TEA) and improvement-driven opportunity entrepreneurial activity. We identify the differences of between necessity-driven and improvement-driven opportunity entrepreneurial activity. For example, the effect on necessity-driven entrepreneurial activity is significantly negative. We also find the differences of between necessity-driven and improvement-driven opportunity entrepreneurial activity in economic environment variables. While real GDP growth as a demand variable has a significantly positive effect on necessity-driven entrepreneurial activity, unemployment rate as a supply variable has a significantly negative effect on early-stage entrepreneurial activity(TEA) and improvement-driven opportunity entrepreneurial activity. And GDP per capita as a supply variable has a significantly positive effect on early-stage entrepreneurial activity(TEA) and improvement-driven opportunity entrepreneurial activity. But the effect on necessity-driven entrepreneurial activity is significantly negative. We provide an interpretation of these empirical findings, emphasizing the importance of considering individual characteristics and economic environment simultaneously in promoting entrepreneurship.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.1-11
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• 2017

The importance of data curation is increasingly recognized as the need of data reuse drastically grows. Due to recent data explosion, scientists invest almost 90% of their efforts in the retrieval and collection of data needed to their study. In this paper, we deal with the development and application of a collaborative environment for community-driven data curation which is essential to enhance scientific data reusability and citability. The collaborative scientific data curation environment focuses on the cross-linking between data (or data collections) and their associated literatures to capture and organize inter-relations among research results in a specific domain. Also, plenty of contextual information is provided as metadata in order to support users in understanding data. The cross-linking has been realized by using DOI system to guarantee global accessibility to data and their relationships to literatures. The curation environment has been adopted to build a community-driven curated DB by a globally well-known intrinsically-disorderd protein research group. The curated DB will drastically reduce researchers' efforts to retrieve and collect the data required for scientific discovery.

• Journal of Korea Water Resources Association
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• v.43 no.3
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• pp.257-264
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• 2010

The amount and continuity of precipitation data used in a hydrological analysis may exert a big influence on the reliability of the analysis. It is a fundamental process to estimate the missing data caused by such as a breakdown of the rainfall recording machine or to expand a short period of rainfall data. In this study a linear programming method treated as a data-driven approach for estimating the missing rainfall data is compared with seven other methods widely used and its superiority is certified. The data used in this research are annual precipitation ones during 17 years at the Cheolwon station including an ungauged period of 15 years and its five surrounding stations. By use of this certified method the ungauged precipitation values at the Cheolweon station are estimated and the areal averages of annual precipitation data for 32 years at the Han River basin are calculated.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.13-22
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• 2019

Various hyper-intelligent and ultra-realistic data-driven services are being increasingly developed with the goal of achieving a hyper-connected intelligent society. To sustain this trend, our research focuses on the integration and optimization of data-driven applications from several aspects such as delivery, storage, execution, and sharing of data and software, beyond the limitations of the existing network infrastructure. In this paper, we present important research issues of data-centric hyper-distributed autonomous infrastructure technologies.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.585-586
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• 2022

사회현안을 해결하기 위한 데이터 분석을 위해서는 많은 양의 데이터 수집과 데이터 분석에 활용할 수 있도록 데이터 전처리가 필요하다. 많은 양의 데이터를 수집 및 처리를 위해 데이터 수집, 데이터 저장, 활용 시스템이 기능적으로 분리하여 시스템을 구성하고, 이에 따른 시스템간의 데이터 상호 연계가 필요하게 된다. 또한 외부 네트워크에 구성되어 있는 시스템간의 데이터 연계나, OpenAPI와 같이 다양한 데이터 서비스에서도 적용이 가능할 수 있도록 확장성과 유연성을 고려할 필요가 있다. 본 논문에서는 부산 지역현안 해결을 위한 시스템 구성에 있어, 확장성을 고려한 데이터 수집 시스템간의 효율적인 데이터 연계 방법을 제안하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2292-2313
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• 2023

With improving computing performance, various digital platforms are being developed to enable easily utilization of high-performance computing environments. EDISON 1.0 is an online simulation platform widely used in computational science and engineering education. As the research paradigm changes, the demand for developing the EDISON 1.0 platform centered on simulation into the EDISON 2.0 platform centered on data and artificial intelligence is growing. Herein, a data framework, a core module for data-centric research on EDISON 2.0 digital platform, is proposed. The proposed data framework provides the following three functions. First, it provides a data repository suitable for the data lifecycle to increase research reproducibility. Second, it provides a new data model that can integrate, manage, search, and utilize heterogeneous data to support a data-driven interdisciplinary convergence research environment. Finally, it provides an exploratory data analysis (EDA) service and data enrichment using an AI model, both developed to strengthen data reliability and maximize the efficiency and effectiveness of research endeavors. Using the EDISON 2.0 data framework, researchers can conduct interdisciplinary convergence research using heterogeneous data and easily perform data pre-processing through the web-based UI. Further, it presents the opportunity to leverage the derived data obtained through AI technology to gain insights and create new research topics.