• Journal of KIISE
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• v.42 no.7
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• pp.840-845
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• 2015

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.93-104
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• 2009

Modular implementation of a feature is a first step toward feature-oriented product line engineering, which develops and then utilizes core assets to configure products in terms of features. Aspect-oriented programming provides effective mechanisms for improving the modularity of feature implementations. However, as features in general are not independent of each other, changes in the implementation of one feature may cause changes to or side effects in the implementation of other features. Moreover, since the time at which a feature is incorporated into products, called feature binding time, may be various from compile time through load time to run time, a feature may have to be implemented differently depending on when the feature is bound into a product. To make each feature implementation module as independent as possible, this paper proposes aspectual implementation patterns that can effectively separate feature dependencies as well as feature binding time from feature implementation modules. These patterns enable flexible composition of feature implementation modules without affecting other modules according to feature selection. The approaches are demonstrated and evaluated based on a product line of scientific calculator applications.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.201-208
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• 2019

In the phono-sapiens era of the intelligence information society, most business activities are increasingly dependent on networks and information systems. SMEs, which occupy the majority of Korean companies, are increasingly possessing the value and technology of their information assets, and their ability to protect core technologies that are the driving force of corporate growth will be the most important competitiveness of enterprises. Accordingly, the Ministry of Science and ICT and the Korea Internet & Security Agency(KISA) provides a foundation for minimizing the damage from cyber threats such as hacking and information leakage by evaluating the current information protection level of SMEs and enhancing information protection capability by supporting a high level of customized information protection consulting. In this study, we analyze the effectiveness of information protection based on the results of KISA SMEs consulting. In addition, by identifying problems and limitations derived from SMEs information protection consulting results, SMEs should propose measures to improve information security of SMEs that can manage information protection management system more efficiently and effectively.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.813-826
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• 2019

This paper is a study on the implementation of MAM(Media Asset Management) to utilize UHD contents as high quality broadcast material. The implementation method of this paper is to separate MAM roles with content management functions and transmission workflow functions from workflow, metadata and system interface related work, which are divided into core MAM and MAM-Ex structure. Through the method proposed in this paper, we improved the content management method by applying the page menu method to the material metadata modification and applying the template method to the material structure API. In addition, the storage of UHD material and the configuration of the component server are pooled without any distinction of channels, thereby enhancing the security of UHD transmission assets by minimizing the movement of contents together with broadcasting material protection.

• Journal of Software Engineering Society
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• v.26 no.2
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• pp.31-44
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• 2013

Software product line (SPL) engineering is a reuse paradigm that helps organizations increase productivity and improve product quality by developing product from reusable core assets. In SPL, product configuration is the process of selecting the desired features and feature attributes for a given product from a feature model. In order to develop a successful product, feature and feature attribute selection that can achieve the product goal is important. There can be thousands of features and feature attributes resulting in myriads of configurations and finding the best configuration efficiently is a hard task. This paper proposes a systematic process for feature-based product configuration. To support development of a product that satisfys all product goals(business goals and quality goals), a model showing how feature and feature attribute combinations are related to product goals is included and a method for deriving an optimal product configuration using the model is proposed.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.45.1-45.1
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• 2021

Horizon Run 5 (HR5) is a cosmological hydrodynamical simulation which captures the properties of the Universe on aGpc scale while achieving a resolution of 1kpc. This enormous dynamic range allows us to simultaneously capture the physics of the cosmic web on very large scales and account for the formation and evolution of dwarf galaxies on much smaller scales. On the back of a remarkable achievement of this, we have finished to run follow-up simulations which have 2 times larger volume than before and are expected to complementary to some limitations of previous HR simulations both for the study on the large scale features and the expansion history in a distant Universe. For these simulations, we consider the sub-grid physics of radiative heating/cooling, reionization, star formation, SN/AGN feedbacks, chemical evolution and the growth of super-massive blackholes. In order to do this project, we implemented a hybrid MPI-OpenMP version of the RAMSES code, 'RAMSES-OMP', which is specifically designed for modern many-core many thread parallel systems. These simulation successfully reproduce various observation result and provide a large amount of statistical samples of Lyman-alpha emitters and protoclusters which are important to understand the formation and expansion history of early universe. These are invaluable assets for the interpretation of current ΛCDM cosmology and current/upcoming deep surveys of the Universe, such as the world largest narrow band imaging survey, ODIN (One-hundred-square-degree Dark energy camera Imaging in Narrow band).

• Management & Information Systems Review
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• v.22
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• pp.137-165
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• 2007

It seems high time to increase productivity by adopting e-business to overcome challenges posed by both external factors including the appreciation of Korean won, oil hikes and fierce global competition and domestic issues represented by disparities between large corporations and small and medium enterprises (SMEs), Seoul metropolitan and local cities, and export and domestic demand all of which weaken future growth engines in the Korean economy. The demands of the globalization era are for innovative changes in businessprocess and industrial structure aiming for creating new values. To this end, e-business is expected to play a core role in the sophistication of the Korean economy through new values and innovation. In order to examine business performance in e-business-adopting industries, this study analyzed the home shopping industry by closely looking into the financial ratios including the ratio of net profit to sales, the ratio of operation income to sales, the ratio of gross cost to sales cost, the ratio of gross cost to selling, general and administrative (SG&A) expense, and return of investment (ROI). This study, for best outcome, referred to corporate financial statements as a main resource to calculate financial ratios by utilizing Data Analysis, Retrieval and Transfer System (DART) of the Financial Supervisory Service, one of the Korea's financial supervisory authorities. First of all, the result of the trend analysis on the ratio of net profit to sales is as following. CJ Home Shopping has registered a remarkable increase in its ratio of net profit rate to sales since 2002 while its competitors find it hard to catch up with CJ's stunning performances. This is partly due to the efficient management compared to CJ's value of capital. Such significance, if the current trend continues, will make the front-runner assume the largest market share. On the other hand, GS Home Shopping, despite its best organized system and largest value of capital among others, lacks efficiency in management. Second of all, the result of the trend analysis on the ratio of operation income to sales is as following. Both CJ Home Shopping and GS Home Shopping have, until 2004, recorded similar growth trend. However, while CJ Home Shopping's operating income continued to increase in 2005, GS Home Shopping observed its operating income declining which resulted in the increasing income gap with CJ Home Shopping. While CJ Home Shopping with the largest market share in home shopping industryis engaged in aggressive marketing, GS Home Shopping due to its stability-driven management strategies falls behind CJ again in the ratio of operation income to sales in spite of its favorable management environment including its large capital. Companies in the Group B were established in the same year of 2001. NS Home Shopping was the first in the Group B to shift its loss to profit. Woori Home Shopping has continued to post operating loss for three consecutive years and finally was sold to Lotte Group in 2007, but since then, has registered a continuing increase in net income on sales. Third of all, the result of the trend analysis on the ratio of gross cost to sales cost is as following. Since home shopping falls into sales business, its cost of sales is much lower than that of other types of business such as manufacturing industry. Since 2002 in gross costs including cost of sales, SG&A expense, and non-operating expense, cost of sales turned out to have remarkably decreased. Group B has also posted a notable decline in the same sector since 2002. Fourth of all, the result of the trend analysis on the ratio of gross cost to SG&A expense is as following. Due to its unique characteristics, the home shopping industry usually posts ahigh ratio of SG&A expense. However, more than 80% of SG&A expense means the result of lax management and at the same time, a sharp lower net income on sales than other industries. Last but not least, the result of the trend analysis on ROI is as following. As for CJ Home Shopping, the curve of ROI looks similar to that of its investment on fixed assets. As it turned out, the company's ratio of fixed assets to operating income skyrocketed in 2004 and 2005. As far as GS Home Shopping is concerned, its fixed assets are not as much as that of CJ Home Shopping. Consequently, competition in the home shopping industry, at the moment, is among CJ, GS, Hyundai, NS and Woori Home Shoppings, and all of them need to more thoroughly manage their costs. In order for the late-comers of Group B and other home shopping companies to advance further, the current lax management should be reformed particularly on their SG&A expense sector. Provided that the total sales volume in the Internet shopping sector is projected to grow over 20 trillion won by the year 2010, it is concluded that all the participants in the home shopping industry should put strategies on efficient management on costs and expenses as their top priority rather than increase revenues, if they hope to grow even further after 2007.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.1-26
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• 2008

Rapid progress of information technology and widespread use of the personal computers have brought various conveniences in our life. But this also provoked a series of problems such as hacking, malicious programs, illegal exposure of personal information etc. Information security threats are becoming more and more serious due to enhanced connectivity of information systems. Nevertheless, users are not much aware of the severity of the problems. Using appropriate password is supposed to bring out security effects such as preventing misuses and banning illegal users. The purpose of this research is to empirically analyze a research model which includes a series of factors influencing the effectiveness of passwords. The research model incorporates the concept of risk based on information systems risk analysis framework as the core element affecting the selection of passwords by users. The perceived risk is a main factor that influences user's attitude on password security, security awareness, and intention of security behavior. To validate the research model this study relied on questionnaire survey targeted on evening class MBA students. The data was analyzed by AMOS 7.0 which is one of popular tools based on covariance-based structural equation modeling. According to the results of this study, while threat is not related to the risk, information assets and vulnerability are related to the user's awareness of risk. The relationships between the risk, users security awareness, password selection and security effectiveness are all significant. Password exposure may lead to intrusion by hackers, data exposure and destruction. The insignificant relationship between security threat and perceived risk can be explained by user's indetermination of risk exposed due to weak passwords. In other words, information systems users do not consider password exposure as a severe security threat as well as indirect loss caused by inappropriate password. Another plausible explanation is that severity of threat perceived by users may be influenced by individual difference of risk propensity. This study confirms that security vulnerability is positively related to security risk which in turn increases risk of information loss. As the security risk increases so does user's security awareness. Security policies also have positive impact on security awareness. Higher security awareness leads to selection of safer passwords. If users are aware of responsibility of security problems and how to respond to password exposure and to solve security problems of computers, users choose better passwords. All these antecedents influence the effectiveness of passwords. Several implications can be derived from this study. First, this study empirically investigated the effect of user's security awareness on security effectiveness from a point of view based on good password selection practice. Second, information security risk analysis framework is used as a core element of the research model in this study. Risk analysis framework has been used very widely in practice, but very few studies incorporated the framework in the research model and empirically investigated. Third, the research model proposed in this study also focuses on impact of security awareness of information systems users on effectiveness of password from cognitive aspect of information systems users.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.601-611
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• 2005

Due to not only proven stability and reliability but a significant investment and years of accumulated -experience and knowledge, legacy systems have supported the core business applications of a number of organizations over many years. While the emergence of Web-based e-business environments requires externalizing core business processes to the Web. This is a competitive advantage in the new economy. Consequently, organizations now need to mine the business value buried in the legacy systems for reuse in new e-business applications. In this paper we suggest a systematic approach to mining components that perform specific business services and that consist of the legacy system's assets to be leveraged on the modem platform. The proposed activities are divided into several tasks. First, use cases that realize the business processes are captured. Secondly, a design model is constructed for each identified use case in order to integrate the use cases with the similar functionalities. Thirdly, we identify component candidates from the design model and then adjust the component candidates by considering common elements among the candidate components. And also business components are divided into three more fine-grained components to deploy them onto J2EE/EJB environments. finally, we define the interfaces of components which provide functionalities of the components as operations.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.