• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.83-105
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• 2013

There are two ways to assess data quality : measurement of data itself and assessment of data quality management process. Recently maturity assessment of data quality management process is used to ensure and certify the data quality level of an organization. Following this trend, the paper presents the process reference model which is needed to assess data quality management process maturity. First, the overview of assessment model for data quality management process maturity is presented. Second, the process reference model that can be used to assess process maturity is proposed. The structure of process reference model and its detail processes are developed based on the process derivation approach, basic principles of data quality management and the basic concept of process reference model in SPICE. Furthermore, characteristics of the proposed model are described compared with ISO 8000-150 processes.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.165-178
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• 2019

In an environment transformed by smart factories, measuring the current level of the manufacturing system, deriving improvement targets and tasks and increasing the level of manufacturing competitiveness become the basic activities of the company. However, research on the component analysis and maturity assessment to ensure the future competitiveness of the company is in progress and in the early stages. This study analyzed the existing research on various models, development process, and framework for manufacturing system. In addition, we designed a structural model by deriving the components of future manufacturing system through smart factory related maturity assessment studies. We designed a meta-model that includes an assesment model and a transformation model, and derived the framework development process to propose an integrated framework for the maturity assessment of the future manufacturing system. We verified it by applying it into an actual evaluation project of smart factory.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.307-309
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• 2005

빠른 산업 발전 속에서 조직과 시스템이 복잡하고 대형화되면서 소프트웨어 품질을 향상시키고 그들의 신뢰성을 측정하기 위한 성숙도 모델들이 개발되었다. 그 중에서도 CMM와 이를 발전시킨 CMMI는 많은 조직에서 적용하기 위해 노력을 기울이고 있다. 하지만 두 모델들은 테스트 속성과 테스트 프로세스에 대한 언급이 부족하다. 본 논문에서는 모든 영역을 고려한 새로운 모델을 만들기 보다는 테스트 중심으로 개발된 TMM을 사용하여 CMMI과의 매핑을 통해 소프트웨어 조직의 생산품질과 신뢰성을 향상 시키는 방법을 제안한다. 논문에서는 CMMI 단점과 이를 보완하는 방법을 제안하고 이를 위해 TMM과 CMMI의 매핑전략에 언급한다. 이를 통해 기대되는 효과를 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.40-42
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• 2003

최근 국내 기업들은 소프트웨어 프로세스 심사를 통하여 기업의 프로세스 성숙도 향상과 개발되는 제품의 품질을 향상시키고자 하는 관점에서 CMM과 SPICE와 같은 프로세스 모텔을 적용하고 있다. 이 모델들은 소프트웨어 프로세스가 일정 수준의 성숙도를 지나면 측정을 활용하여 성숙도를 높일 수 있도록 모델을 제시하고 있으며, 이는 소프트웨어 개발의 지표들을 정량적인 수치로 객관화하여 관리하도록 권고하고 있다. 본 논문에서는 Embedded 소프트웨어를 개발하는 기업에서 측정을 통하여 지표들을 정량적으로 관리하고 각 지표들 간의 상관 관계를 분석하여 메트릭스와 시장 점유율간의 의존도 분석 사례를 제시한다.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.87-98
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• 2020

Recently, IoT, artificial intelligence, cloud, big data, and mobile fields have converged, leading to a new industrial era called the 4th industrial revolution. This 4th industrial revolution has been expanded to all industry area and Software has been taken as important role in this revolution. Thus Software Safety is the huge factor because Software is highly relevant to human safety in accordance with Software expansion. However this Software Safety has been focused on not organization improvement activities but current design/development, In this paper, to solve this issue, Software Safety Maturity level and relevant Process Area is defined. This study is expected to contribute to systematic software safety activities.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.487-489
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• 2004

소프트웨어 테스트는 소프트웨어 개발 과정에 매우 중요한 요소이다. 현재 많은 조직들이 테스트 성숙도를 높이기 위한 노력을 진행하고 있다. 그 중에서 일리노이공대의 TMM(Test Maturity Model)이 대표적이라고 볼 수 있다. 하지만 TMM은 바로 테스트 성숙수준을 측정하는데 문제가 있다. 그래서 TMM과 테스트 프로세스를 향상시킬 수 있는 TPI(Test Process Improvement) 모델을 통해, 우리는 Enhanced-TMM에 테스트 속성과 성숙도 상호관련 매트릭스를 사용한 테스트 성숙수준 측정 방법을 구체적으로 제안하고자 한다.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.103-110
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• 2023

In this study, clustering analysis was performed to find out the influence of the maturity level of Industry 4.0 of SMEs in Korea, index factors of clustering, and major factors on the self-evaluation of companies. When 80 domestic SMEs were classified into 4 categories, it was found that there was a significant positive correlation between process, technology and organization. In addition, the majority of the 80 companies tested according to the maturity model appear to be immature or partially mature, and many improvements and re-evaluation of innovation strategies related to Industry 4.0 are needed. Finally, it was concluded that the Singapore Smart Industry Readiness Index is suitable for conducting self-assessment in domestic SMEs. These conclusions can serve as useful maturity and grouping guidelines for practitioners and researchers.

• Informatization Policy
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• v.22 no.1
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• pp.28-46
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• 2015

Although the demand for the use of public data increases in accordance with the expansion of Government 3.0, the poor level of data quality and its management currently implemented is becoming obstacles to opening data to the public. To improve the efficiency of management, linkage and usage for data, standardized processes for data quality management have to be prepared and appropriate data quality assessment criteria should be established. In this paper, we propose the organizational maturity model that can assess the public data quality management level. This model consists of the process reference model and the measurement framework. Fifteen processes grouped by the PDCA cycle are defined in the process reference model. The measurement framework measures the organizational maturity level based on process capability levels. The organizational maturity model can be used to establish objectives and directions for public data quality improvement by diagnosis of current level of public data quality management and problem solving. This model can also facilitate open to the private sector and activate usage of stable public data through reliability enhancement.

• Informatization Policy
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• v.20 no.1
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• pp.85-109
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• 2013

This study presents a model and its implementation methodology assessing not only maturity but also applicability in order to increase the practical usage of open source software(OSS). Since this model measures applicability which can not be assessed by the previous models, it is able to support users in many ways when they introduce OSS for the first time, replace commercial software with OSS, and compare OSS with commercial software. For the purpose of design and implementation of this model, we presented a methodology with 7 phases and conducted a survey to evaluate the appropriateness and reliability of the model. In addition, we proved the practical usability of the model by providing a practical example assessing maturity and applicability of OSS in the area of the infra category on the basis of the methodology. In the future, not only general users but also officers in charge of informatization will be able to select optimal OSS by using this model for industrial application. Therefore, the result of this research is expected to greatly contribute to OSS promotion policy.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.219-227
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• 2020

Smart Factory is one of the fastest developing and changing fourth industrial revolution fields. In particular, the degree of introduction and maturity level in the smart factory is an important part. In this paper, a cluster analysis of companies introduced smart factory was performed based on a new maturity assessment model. The 68% of 193 companies surveyed were at the basic level, with only 21% being the middle one. Most SMEs cited lack of funds as the main reason for not entering the middle one. As a result of the cluster analysis, it was found that all clusters had similar patterns but grouped into one of three levels of high, middle, and low depending on maturity level of smart factory operation, and process domain had the highest maturity and data domain was lowest among the 6 domains. Through this, analysis of more specific and quantified maturity levels can be performed using 6-domain smart factory maturity evaluation model.