• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.151-160
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• 2007

A datawarehouse which extracts and saves the massive analysis data is used for marketing and decision support of business. However, the datawarehouse has the problem of increasing the process time and cost as well as has a high risk of process errors because it integrates vast amount of data from distributed environments. Thus, we propose a metrics for measurement in the area of productivity, process quality and data quality. Also through the evaluation using the proposed metrics, we show that our proposal provides productivity enhancement and process improvement.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.79-116
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• 2008

This paper examines and estimates GARCH-VaR models (RiskMetrics, GARCH, IGARCH, GJR and APARCH) with three different distributions such as Gaussian normal, Student-t, Skewness Student-t Distribution using the daily price data from Korean Stock Market during Jan. 1, 1980-Sept. 30, 2004. It also compares them. In-sample test, this finds that for all confidence level as $90%{\sim}99.9%$, the performance and accuracy of IGARCH with ${\lambda}=0.87$ and skewness Student-t distribution are superior to other models and distributions in long position, but GARCH and GJR with Skewness Student-t distribution in short position. For above 99% confidence level, the performance and accuracy of IGARCH with ${\lambda}=0.87$ in both long and short positions are superior to other models and distributions, but Skewness Student-t distribution for long position and Student-t distribution for short position are more accuracy and superior to other distributions. In-out-of sample test, these results also confirm the evidences that the above findings are consistent as well.

• Acute and Critical Care
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• v.33 no.4
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• pp.222-229
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• 2018

Background: The diagnosis of pediatric acute respiratory distress syndrome (PARDS) is a pragmatic decision based on the degree of hypoxia at the time of onset. We aimed to determine whether reclassification using oxygenation metrics 24 hours after diagnosis could provide prognostic ability for outcomes in PARDS. Methods: Two hundred and eighty-eight pediatric patients admitted between January 1, 2010 and January 30, 2017, who met the inclusion criteria for PARDS were retrospectively analyzed. Reclassification based on data measured 24 hours after diagnosis was compared with the initial classification, and changes in pressure parameters and oxygenation were investigated for their prognostic value with respect to mortality. Results: PARDS severity varied widely in the first 24 hours; 52.4% of patients showed an improvement, 35.4% showed no change, and 12.2% either showed progression of PARDS or died. Multivariate analysis revealed that mortality risk significantly increased for the severe group, based on classification using metrics collected 24 hours after diagnosis (adjusted odds ratio, 26.84; 95% confidence interval [CI], 3.43 to 209.89; P=0.002). Compared to changes in pressure variables (peak inspiratory pressure and driving pressure), changes in oxygenation (arterial partial pressure of oxygen to fraction of inspired oxygen) over the first 24 hours showed statistically better discriminative power for mortality (area under the receiver operating characteristic curve, 0.701; 95% CI, 0.636 to 0.766; P<0.001). Conclusions: Implementation of reclassification based on oxygenation metrics 24 hours after diagnosis effectively stratified outcomes in PARDS. Progress within the first 24 hours was significantly associated with outcomes in PARDS, and oxygenation response was the most discernable surrogate metric for mortality.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.947-978
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• 2007

In this paper, we investigated a Value-at-Risk approach to the volatility of two crude oil markets (Brent and Dubai). We also assessed the performance of various VaR models (RiskMetrics, GARCH, IGARCH and FIGARCH models) with the normal and skewed Student-t distribution innovations. The FIGARCH model outperforms the GARCH and IGARCH models in capturing the long memory property in the volatility of crude oil markets returns. This implies that the long memory property is prevalent in the volatility of crude oil returns. In addition, from the results of VaR analysis, the FIGARCH model with the skewed Student-t distribution innovation predicts critical loss more accurately than other models with the normal distribution innovation for both long and short positions. This finding indicates that the skewed Student-t distribution innovation is better for modeling the skewness and excess kurtosis in the distribution of crude oil returns. Overall, these findings might improve the measurement of the dynamics of crude oil prices and provide an accurate estimation of VaR for buyers and sellers in crude oil markets.

• Journal of Information Processing Systems
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• v.12 no.2
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• pp.226-233
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• 2016

Cloud computing is a distributed computing model that has lot of drawbacks and faces difficulties. Many new innovative and emerging techniques take advantage of its features. In this paper, we explore the security threats to and Risk Assessments for cloud computing, attack mitigation frameworks, and the risk-based dynamic access control for cloud computing. Common security threats to cloud computing have been explored and these threats are addressed through acceptable measures via governance and effective risk management using a tailored Security Risk Approach. Most existing Threat and Risk Assessment (TRA) schemes for cloud services use a converse thinking approach to develop theoretical solutions for minimizing the risk of security breaches at a minimal cost. In our study, we propose an improved Attack-Defense Tree mechanism designated as iADTree, for solving the TRA problem in cloud computing environments.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.269-279
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• 2022

With the development of artificial intelligence, artificial intelligence is being introduced to automate systems throughout the industry. In the maritime industry, artificial intelligence is being applied step by step, through the paradigm of autonomous ships. In line with this trend, ABS and DNV have published guidelines for autonomous vessels. However, there is a possibility that the risk of artificial intelligence has not been sufficiently considered, as the classification guidelines describe the requirements from the perspective of ship operation and marine service. Thus in this study, using the standards established by the ISO/ IEC JTC1/SC42 artificial intelligence division, classification requirements are classified as the causes of risk, and a measure that can evaluate risks through the combination of risk causes and artificial intelligence metrics want to use. Through the combination of the risk causes of artificial intelligence proposed in this study and the characteristics to evaluate them, it is thought that it will be beneficial in defining and identifying the risks arising from the introduction of artificial intelligence into the marine system. It is expected that it will enable the creation of more detailed and specific safety requirements for autonomous ships.

• Journal of the Korea Society of Computer and Information
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• v.7 no.2
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• pp.87-94
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• 2002

This paper implemented the tools to measure the risk of the modules for the web application project written in ASP, using the Indicator of the Module Risk Levels. The Indicator of that is developed by GSFC group in NASA based on structural programming language and gives us software product quality metrics. The implemented tools examined with the ASP projects of the practical business. As the results the data shows the module risk levels easily, and then the data affects the maintenance to improve the application quality.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.4
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• pp.37-44
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• 2021

In case of South Korea, the airspace which airlines can operate is extremely limited due to the military operational area located within the Incheon flight information region. As a result, safety problems such as mid-air collision between aircraft or Traffic alert and Collision Avoidance System Resolution Advisory (TCAS RA) may occur with higher probability than in wider airspace. In order to prevent such safety problems, an mid-air collision risk detection model based on Detect-And-Avoid (DAA) well clear metrics is investigated. The model calculates the risk of mid-air collision between aircraft using aircraft trajectory data. In this paper, the practical use of DAA well clear metrics based model has been validated. Aviation safety data such as aviation safety mandatory report and Automatic Dependent Surveillance Broadcast is used to measure the performance of the model. The attributes of individual aircraft track data is analyzed to correct the threshold of each parameter of the model.

• Korea Journal of Hospital Management
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• v.23 no.1
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• pp.87-106
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• 2018

Objective: The objective of this study is to examine the association between process quality of inpatient care and risk-adjusted, thirty-day potentially preventable hospital readmission (PPR) rates. Data Sources/Study Setting: This was an observational cross-sectional study of nonfederal acute-care hospitals located in two states California and Florida, discharging Medicare patients with a principal discharge diagnosis of heart failure, acute myocardial infarction, or pneumonia January through December 31, 2007. Data were obtained from the Healthcare Cost and Utilization Project State Inpatient Database of the Agency for Healthcare Research and Quality, Centers for Medicare and Medicaid Services Hospital Compare database, and the American Hospital Association Annual Survey of Hospitals. Study Design: The dependent variable of this study is condition-specific, risk-adjusted, thirty-day potentially preventable hospital readmission (PPR). 3M's PPR software was utilized to determine whether a readmission was potentially preventable. The independent variable of this study is hospital performance for process quality of inpatient care, measured by hospital adherence to recommended processes of care. We used multivariate hierarchical logistic models, clustered by hospitals, to examine the relationship between condition-specific, risk-adjusted, thirty-day PPR rates and process quality of inpatient care, after taking clinical and socio-demographic characteristics of patients and structural and operational characteristics of hospitals into account. Findings: Better performance on the process quality metrics was associated with better patient outcome (i.e., low thirty-day PPR rates) in pneumonia, but not generally in two cardiovascular conditions (i.e., heart failure and acute myocardial infarction). Practical Implication: Adherence to the process quality metrics currently in use by CMS is associated with risk-adjusted, thirty-day PPR rates for patients with pneumonia, but not with cardiovascular conditions. More evidence-based process quality metrics closely linked to 30-day PPR rates, particularly for cardiovascular conditions, need to be developed to serve as a guideline to reduce potentially preventable readmissions.

• The Journal of Health Technology Assessment
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• v.1 no.1
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• pp.22-26
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• 2013

Objectives: Balancing benefits and risks through the drug life cycle has been discussed for many decades. The objective of this study was to review the processes and tools currently proposed for benefit-risk assessment of medicinal drugs. It aimed to establish scientific and efficient drug safety management system based on the synthetic analysis of benefit-risk evidence. Methods: We conducted a review of exiting literatures published by regulatory agencies or initiatives. Not only quantitative methodologies but also qualitative method were compared to understand their key characteristics for the benefit and risk assessment of drugs. Results: Recently, benefit-risk assessments have more structured approaches to decision making as part of regulatory science. Regulatory agencies such as European Medicines Agency, FDA have prepared plans to apply benefit-risk assessment to regulatory decision making. Also many initiatives such as IMI (Innovative Medicine Initiative) have conducted research and published reports about benefit-risk assessment. For benefit-risk assessment, four kinds of methods are necessary. Frameworks such as BRAT (Benefit Risk Action Team) framework, PrOACT-URL provide guidance for the whole process of decision-making. Metrics are measurements of risk benefit. The estimation techniques are methods to synthesis and combine evidences from various sources. The utility survey techniques are necessary to explicit preferences of various outcome from stakeholders. Conclusion: There is the lack of widely accepted, validated model for benefit-risk assessment. Nor there is an agreement among academia, industry, and government on methods for the quantitative valuation. It is also limited by available evidence and underlying assumptions. Nevertheless, benefit-risk assessment is fundamental to improve transparency, consistency and predictability for decision making through the structured systematic approaches.