• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.220-229
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• 2020

With the recent development of digital technology, the research paradigm is evolving towards data-driven. National management and utilization of research data is a key element not only to enhance research transparency and efficiency, but also to prepare for a data-driven society. Policies and infrastructure for sharing and utilization of research data from publicly-funded research are being actively promoted worldwide. In Korea, related regulations were recently revised to mandate to submit a data management plan (DMP) when proposing a national R&D project. In order to effectively implement the sustainable DMP system, researchers need various support. In addition, guidelines and implementation procedures are essential for management and utilization of research data at the national or institutional level. In this paper, we provide an overview of the data management plan, examine the current status and issues in Korea, and suggest a template and checklists of data management plan, and an implementation procedure at research institutes.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.45-55
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• 2012

Electronic data management is getting important to reduce overall cost and run-time of clinical data management with the enhancement of data quality. It also critically needs to meet regulated guidelines for the overall quality and safety of electronic clinical trials. The purpose of this paper is to develop the performance evaluation framework in electronic clinical data management. Four key metrics in the area of infrastructure, intellectual preparation, study implementation and study completion covering major aspects of clinical trial processes are proposed. The performance measures evaluate the extent of regulation compliance, data quality, cost and efficiency of electronic data management process. They also provide measurement indicators for each evaluation items. Based on the key metrics, the performance evaluation framework is developed in three major areas involved in clinical data management - clinical site, monitoring and data coordinating center. As of the initial attempt how to evaluate the extent of electronic data management in clinical trials by Delphi survey, further empirical studies are planned and recommended.

• Journal of Korean Society of Archives and Records Management
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• v.20 no.4
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• pp.41-58
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• 2020

Although numerous studies have noted the importance of data sets in government information systems, the practical management of data sets has yet to be developed. Under these circumstances, the National Archives of Korea designated data set management as a major project in 2020, initiating full-scale management work. Despite these efforts, the records center, which will conduct management, expressed great concern for the new project. As such, this study identifies problems in managing data sets and searches for possible improvements through a comparison with existing public data projects by public institutions. In particular, the following materials were analyzed: laws, notices, guidelines, and publications issued by the ministries. Based on the results, several measures were proposed as part of an improvement plan for data set management: (1) the utilization of government functional classification as a reference, (2) the reorganization of the table, and (3) data linkage with related systems.

• Journal of Computing Science and Engineering
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• v.10 no.1
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• pp.21-31
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• 2016

With the application and development of biomedical techniques such as next-generation sequencing, mass spectrometry, and medical imaging, the amount of biomedical data have been growing explosively. In terms of processing such data, we face the problems surrounding big data, highly intensive computation, and high dimensionality data. Fortunately, cloud computing represents significant advantages of resource allocation, data storage, computation, and sharing and offers a solution to solve big data problems of biomedical research. In order to improve the efficiency of resource management in cloud computing, this paper proposes a clustering method and adopts Radial Basis Function in order to compress comprehensive data sets found in biology and medicine in high quality, and stores these data with resource management in cloud computing. Experiments have validated that with such a data-compression-based resource management in cloud computing, one can store large data sets from biology and medicine in fewer capacities. Furthermore, with reverse operation of the Radial Basis Function, these compressed data can be reconstructed with high accuracy.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.73-77
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• 2021

As the big data analysis technologies has been developed worldwide, the importance of asset management for electric power facilities based data analysis is increasing. It is essential to secure quality of data that will determine the performance of the RISK evaluation algorithm for asset management. To improve reliability of asset management, asset data must be preprocessed. In particular, the process of cleaning dirty data is required, and it is also urgent to develop an algorithm to reduce time and improve accuracy for data treatment. In this paper, the result of the development of an automatic cleaning algorithm specialized in overhead transmission asset data is presented. A data cleaning algorithm was developed to enable data clean by analyzing quality and overall pattern of raw data.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.245-256
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• 2021

The Fourth Industrial Revolution brought the quantitative value of data across the industry and entered the era of 'Big Data'. This is due to both the rapid development of information & communication technology and the diversity & complexity of customer purchasing tendencies. An enterprise's core competence in the Big Data Era is to analyze and utilize the data to make strategic decisions for enterprise. However, most of traditional studies on Big Data have focused on technical issues and future potential values. In addition, these studies lacked interest in managing the quality and utilization levels of internal & external customer Big Data held by the entity. To overcome these shortages, this study attempted to derive influential factors by recognizing the quality management information systems and quality management of the internal & external Big Data. First of all, we conducted a survey of 204 executives & employees to determine whether Big Data quality management, Big Data utilization, and level management have a significant impact on corporate work efficiency & corporate management performance. For the study for this purpose, hypotheses were established, and their verifications were carried out. As a result of these studies, we found that the reasons that significantly affect corporate management performance are support from the management class, individual innovation, changes in the management environment, Big Data quality utilization metrics, and Big Data governance system.

• Journal of the Korean Dietetic Association
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• v.23 no.2
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• pp.192-201
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• 2017

This study compared the Center for Children's Foodservice Management in 2012, 2014, and 2016 using big data and opinion mining. The data on the Center for Children's Foodservice Management were collected from the portal site, Naver, from January 1 to December 31 in 2012, 2014, & 2016 and analyzed by keyword frequency analysis, influx route analysis of data, polarity analysis via opinion mining, and positive and negative keyword analysis by polarity analysis. The results showed that nursery had the highest rank every year and education supported by Center for Children's Foodservice Management has increased significantly. The influx of data has increased through the influx route analysis of data. Blog and $caf\acute{e}e$, which have a considerable amount of information by the mother should be helpful for use as public relations and participation recruitment paths. By polarity analysis using opinion mining, the positive image of the Center for Children's Foodservice Management was increased. Therefore, the Center for Children's Foodservice Management was well-suited to the purpose and the interests of the people has been increasing steadily. In the near future, the Center for Children's Foodservice Management is expected have good recognition if various programs to participate with family are developed and advertised.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.397-400
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• 2000

This paper provides a product data representation, a data schema and related operations, for the management of engineering changes. The computer Interpretable format of the representation enables itself to be realized through databases and their applications to a component of a product data management system. Supporting general operations for the engineering changes, it resolves problems of the existing information systems in the nested engineering changes and the simultaneous change application to multiple products. To show the feasibility, a prototype system is implemented based on the representation.

• Journal of Korean Society for Quality Management
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• v.22 no.3
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• pp.152-160
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• 1994

This paper concerns an expert system for reliability management. The system includes data base, life data analysis, life testing sampling plans and system operation. PROLOG is used as a language with dBASE III+ for the data base management system and C for calculations and graphics. This system analyzing the data and selecting an appropriate sampling plan can be implemented on an IBM PC 386 or a higher level machine.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.151-175
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• 2024

This study analyzes the current status of research data management at NST-affiliated government-funded research institutes for the purpose of promoting the sharing and use of research data, and based on this, suggests methods for establishing a research data sharing and management system. The survey on the status of research data management was conducted twice in 2022 and 2023 for a total of 20 research institutes. In addition, difficulties and areas that need to be improved in the management and sharing of research data were identified, and based on this, methods for establishing a research data sharing and management system were proposed by dividing them into policy aspects, system aspects, and linkage system construction aspects. In order to establish a research data sharing system, it would be desirable to prepare a policy basis and present contents such as the definition of research data, scope of application, contents of management, utilization method, and leading institutes. In addition, for systematic and unified research data management, it would be recommended that each institute will establish and manage a repository and management system. By linking this with DataON, the national research data platform, and providing one-stop services, the accessibility and usability of data will be improved.