• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.213-231
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• 2023

The purpose of this study is to develop and present a model that can evaluate the data maturity of library. To achieve this goal, library data maturity model can be applied to library was designed by analyzing previous studies related to data maturity. As a result of this study, proposed data maturity model consisting of 19 evaluation factors in 5 areas was designed, and the maturity level was set to 5 levels. In the future, it will be possible to measure the data maturity of libraries participating in the library big data project using the data maturity evaluation model, and it can be expected that in the long term, it will be possible to present a direction for data-based library operation and data utilization development.

• Informatization Policy
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• v.28 no.1
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• pp.43-63
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• 2021

The purpose of this research is to examine the relationships among data literacy, organizational culture, and data analytics maturity and the moderating effects of organizational culture. Analysis of the relationship between data literacy and data analytics maturity shows that the higher the data literacy competency of employees, the higher the organization's data analytics maturity. In examining the relationship between organizational culture and data analytics maturity, it is found that relationship culture and innovation culture are positively related to data analytics maturity. In addition, relationship culture and hierarchy culture show significant moderating effects. Relationship culture shows a synergistic effect, whereas hierarchy culture has a buffer effect between data literacy and data analytics maturity.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.461-468
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• 2022

Development of the big data industry, the cases of providing data utilization services on digital platforms are increasing. In this regard, research in data-related fields is being conducted to apply the FAIR principle that can be applied to the assessment of (meta)data quality, service, and function to data quality evaluation. Especially, the European Open Data Portal applies an assessment model based on FAIR principles. Based on this, a data maturity assessment is conducted and the results are disclosed in reports every year. However, public data portals do not conduct data maturity evaluations based on metadata. In this paper, we propose and evaluate a new model for data maturity evaluation on a big data platform built for multiple domestic public data portals and data transactions, FAIR principles used for data maturity evaluation in Europe's open data portals. The proposed maturity evaluation model is a model that evaluates the quality of public data portal datasets.

• The Journal of Bigdata
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• v.6 no.1
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• pp.13-22
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• 2021

The purpose of this study is to develop and demonstrate a framework for evaluating the maturity of big data for effective data strategy establishment and efficient investment of companies. By supplementing the shortcomings of the evaluation developed so far, a framework was developed to evaluate the maturity of a company's big data in an integrated process. As a result, four evaluation areas of 'Vision and Strategy', 'Management', 'Analysis' and 'Utilization', assessment items for each area, detailed content, and criteria for each stage were derived. This was verified through a survey of entrepreneurs, and the maturity level of big data of domestic companies was confirmed. As a future research direction, it is proposed to develop detailed assessment factors according to the characteristics of each industry, to develop a data utilization framework according to the assessment results, and to improve validity and reliability through adjustment of verification targets.

• 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.

• 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.

• Journal of the Korean Society for information Management
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• v.20 no.4 s.50
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• pp.249-275
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• 2003

In companies competing for today's information society, Data quality deterioration is causing a negative influence to generate company competitiveness fall and new cost. A lot of Preceding study about data qualify have been proceeded in order to solve a problem of these data qualify deterioration. Among the sides of data qualify, it has been studied mainly on qualify of the data valve and quality of data service that are the results quality concept. However. this study studied structural qualify of the data which were cause quality concept in a viewpoint of meta data management and presented data quality management maturity model through this. Also empirically this study verified that data quality improved if the management level matured.

• Informatization Policy
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• v.24 no.1
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• pp.30-47
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• 2017

The Korean government developed an organizational maturity model for public data quality management based on international standards to evaluate the data quality management level of public organizations, However, as the model has too many indicators to apply on the site, a new model with reduced number of indicators is proposed in this paper. First, the number of processes is reduced by integrating and modifying the processes of the previous model. Second, a new maturity evaluation method is proposed based on capability levels focused on the activity, not on the process. Third, the maturity level of public data quality management is represented by five discrete levels or real values of 1 through 5. Finally, characteristics of the proposed model are compared with those of the previous model.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.153-154
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• 2022

본 논문에서는 역연령으로 구분되었던 기존 생물학적 성숙도에 기반한 체력 지표가 아닌 골성숙도를 활용한 생물학적 성숙도에 기반하여 유소년의 신체에 맞는 체계적인 운동을 추천하는 기법을 제안한다. 이를 통해 유소년의 성장기에 개인화된 운동능력 발달을 성취하게 함으로써 국민 체력 증진에 기여하고 체육 공교육 활성화 및 유소년 피트니스 관리 산업 발전에 도움이 될 것으로 기대한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.89-95
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• 2023

The underground utility tunnel is an urban infrastructure that accommodates and manages important facilities such as water and sewage, electricity, and communication in the city, and is a national facility that needs to be protected from disasters such as fire, earthquake, and flooding. In establishing a disaster safety life cycle management system such as prediction, prevention, preparedness, response, and recovery, a disaster safety management platform for underground utility tunnel is being developed by utilizing digital twin technology in which advanced ICT technology and data are converged. In this paper, the maturity model for the disaster safety digital twin was reviewed, and the datasets necessary for implementing the digital twin at each stage were defined.