• Knowledge Management Research
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• v.22 no.2
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• pp.209-225
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• 2021

Product Lifecycle Management is a well-defined management method consisting of 8 knowledge areas. Since the 4th industrial revolution is closely related to smart factories, the importance of product lifecycle management, which effectively manages the entire process from product idea generation to disposal, is emerging. This study analyzed the current and future applications of the knowledge domain of product life cycle management from the perspective of the 4th industrial revolution for experts in the field of product life cycle management. The expert's perception was analyzed from the current point of view and the future point of view to see how the product life cycle management knowledge area is applied in the field. The current and future application degree of the knowledge domain of product life cycle management was analyzed, and whether there was a difference between the knowledge domains in terms of the level of application was analyzed. Based on the analyzed results, its meaning and future flow are presented.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.55-62
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• 2018

American charter schools are independent public schools of choice, freed from rules but accountable for results. Charter schools have celebrated the 25th anniversary of its creation in 2017 and become part of landscape of public education in America. However, little research has been conducted on an individual charter school. This study aimed to examine the major factors of a successful charter school. A qualitative approach was employed. Seven one-hour in-depth interviews were conducted with semi-structured interview questions. And four teachers were participated. The conclusions are as follows: First, there is the combination of system and cultural factors as major successful factors of the Dayton Early College Academy. Second, system factors are the gateways and the advisory that is a twin at the charter school. The findings are considered to be applied for Korean educational settings and the implications can be used for policy development in Korea.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.46-51
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• 2015

The explosive growth of data and the rapidly changing technical social evolution new analysis paradigm for predicting and reacting the future the past and present ig data. Prescriptive analysis has a fundamental difference because can support specific behaviors and results according to user's goals with defin researchers establish judgments and activities achiev the goals. However research methods not widely implemented and even the terminology, Prescriptive analysis, is still unfamiliar. This paper thus propose an infrastructure in the prescriptive analysis field with key considerations for enhancing capability of researchers through a case study based on InSciTe Advisory developed with scientific big data. InSciTe Advisory system s developed in 2013, and offers a prescriptive analytics report which contains various As-Is analysis results and To-Be analysis results 5W1H methodology. InSciTe Advisory therefore shows possibility strategy aims to reach a target role model group. Through the availability and reliability of the measurement model the evaluation results obtained relative advantage of 118.8% compared to Elsevier SciVal.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.