• Journal of Korean Library and Information Science Society
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• v.51 no.2
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• pp.103-131
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• 2020

This study was started with the aim of grasping the current status of research data management of forestry engineering researchers. In order to achieve the research purpose, the survey was conducted using a tool called DAF (Data Asset Framework). DAF is an investigative tool that provides a means to identify, position, describe and evaluate how the agency manages research data. Using this DAF, the research data management status was analyzed for researchers in the field of forestry engineering at the National Institute of Forest Science. As a result of analysis, the current status and problems of the five categories such as the method and type of research data creation, sharing, storage, preservation, and reuse were identified, and solutions were presented in relation to the problems. This study is a basic investigation using a systematic tool such as DAF, and can be used as a reference for analyzing the current status and problems of research data when designing RDM system in a specific field.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2473-2478
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• 2011

The asset management of railway facilities is a total framework for finally supporting a safe and comfortable train service, which includes functions of supporting evaluation of condition and performance of infrastructures, making the decision method of repair or rehabilitation of deteriorated facilities, and lengthening the life cycle of structure through the decision of adequate cost and time of repair or reinforcement. In the range of the asset management, organization, human, the target, and information & data of company are included. Therefore, in this paper, appling the method of asset management analysis to the railway structures, the process of the risk assesment using BRE(Business Risk Exposure) and the basis of consisting optimized renewal decision-making are expressed.

• Journal of the Korean Society for information Management
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• v.37 no.3
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• pp.77-106
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• 2020

In this study, we proposed a method to systematically manage research data in the field of condensed matter physics, which is the most active and interdisciplinary field. In the course of the research, a questionnaire was conducted for researchers in the field of condensed matter physics. The questionnaire was constructed based on the research data management tool Data Asset Framework (DAF) and the FAIR principle for data sharing and reuse. The current status of research data management in the field of aggregated material physics was collected from 14 researchers. The collected data consisted of data on the characteristics and basic information of researchers who answered the questionnaire, data preservation and management, and data sharing and access. By analyzing the collected questionnaire results, nine problems were drawn about the characteristics of research data in the field of aggregate material physics, data collection and production, data preservation and management, data sharing and access. In this study, suggestions were made to improve the problems derived from each aspect.

• Knowledge Management Research
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• v.19 no.1
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• pp.133-146
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• 2018

This study develops an Education framework for users who need public open data for workplace etiquette education in a timely manner by mobile application. It facilitates utilizing efficiently Workplace etiquette contents that scattered in various platforms such as blogs, Youtube and web-sites run by private education agencies. Furthermore, it makes Public open data for workplace etiquette through gathering 'metadata', which is a comprehensive source of workplace etiquette. Accordingly, framework changes recognition about necessity of workplace etiquette education positively and suggests method that can promote effective workplace etiquette education. If the system in the study can provide public open data of workplace etiquette education, many young job applicants and workers will have a proper perception on it and sound workplace etiquette culture will be settled in the companies. Public data has been rising as a vital national strategic asset these days. Hopefully the public data will pave a way to discover the blue ocean in the market and open up a new type of businesses.

• Journal of Information Technology Services
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• v.23 no.1
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• pp.1-10
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• 2024

The IT environment is changing due to the acceleration of digital transformation in enterprises and organizations. This expansion of the digital space makes centralized cybersecurity controls more difficult. For this reason, cyberattacks are increasing in frequency and severity and are becoming more sophisticated, such as ransomware and digital supply chain attacks. Even in large organizations with numerous security personnel and systems, security incidents continue to occur due to unmanaged and unknown threats and vulnerabilities to IT assets. It's time to move beyond the current focus on detecting and responding to security threats to managing the full range of cyber risks. This requires the implementation of asset Inventory for comprehensive management by collecting and integrating all IT assets of the enterprise and organization in a wide range. IT Asset Management(ITAM) systems exist to identify and manage various assets from a financial and administrative perspective. However, the asset information managed in this way is not complete, and there are problems with duplication of data. Also, it is insufficient to update of data-set, including Network Infrastructure, Active Directory, Virtualization Management, and Cloud Platforms. In this study, we, the researcher group propose a new framework for automated 'Comprehensive IT Asset Management(CITAM)' required for security operations by designing a process to automatically collect asset data-set. Such as the Hostname, IP, MAC address, Serial, OS, installed software information, last seen time, those are already distributed and stored in operating IT security systems. CITAM framwork could classify them into unique device units through analysis processes in term of aggregation, normalization, deduplication, validation, and integration.

• Smart Media Journal
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• v.8 no.2
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• pp.94-98
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• 2019

In this paper, we developed the tools enabling to manage the IoT systems owned by managers. Since equipment agents consists based on open-source block-chain framework, we can secure the invariance on data and furthermore can locate the resources by searching the AP connected to the equipments. Also the manager can trace the connecting details on equipments from their block-chain accounts. In addition, we work on the possibility of protecting ARP poisoning attacks by removing the credibility on additional ARP requests being generated during the process of network creation.

• Journal of Construction Engineering and Project Management
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• v.7 no.1
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• pp.30-36
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• 2017

Most local agencies such as counties and small cities continuously express difficulties in making technically and financially defensible decisions on their pavement infrastructure maintenance and rehabilitation. Unlike pavement systems managed by state highway agencies, the total lane-miles of many local pavements are significantly short and they are managed by a limited number of staff who typically have multiple responsibilities. Most local agencies also do not have historical pavement performance data and the lack of a systematic decision making framework exacerbates the problem. A structured framework and an easily accessible decision support tool that reflects their local requirements, practices and operational conditions would greatly assist them in making consistent and defensible decisions. This study fills this gap by developing a systematic pavement treatment selection framework and a spreadsheet based tool for local agencies. It is expected that the proposed framework will significantly help local agencies to improve their pavement asset management practices at the project level.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• ETRI Journal
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• v.33 no.3
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• pp.443-453
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• 2011

This paper evaluates the efficiency of mobile content firms through a hybrid approach combining data envelopment analysis (DEA) to analyze the relative efficiency and performance of firms and principal component analysis (PCA) to analyze data structures. We performed a DEA using the total amount of assets, operating costs, employees, and years in business as inputs, and revenue as output. We calculated fifteen combinations of DEA efficiency in the mobile content firms. We performed a PCA on the results of the fifteen DEA models, dividing the mobile content firms into those having either 'asset-oriented' or 'manpower and experience-oriented' efficiency. Discriminant analysis was used to validate the relationship between the efficiency models and mobile content types. This paper contributes toward the construction of a framework that combines the DEA and PCA approaches in mobile content firms for use in comprehensive measurements. Such a framework has the potential to present major factors of efficiency for sustainable management in mobile content firms and to aid in planning mobile content industry policies.

• Journal of Information Technology Applications and Management
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• v.22 no.3
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• pp.1-18
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• 2015

Financial markets are characterized by large numbers of complex and interacting factors which are ill-understood and frequently difficult to measure. Mathematical models developed in finance are precise formulations of theories of how these factors interact to produce the market value of financial asset. While these models are quite good at predicting these market values, because these forces and their interactions are not precisely understood, the model value nevertheless deviates to some extent from the observable market value. In this paper we propose a framework for augmenting the predictive capabilities of mathematical model with a learning component which is primed with an initial set of historical data and then adjusts its behavior after the event of prediction.