• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.159-169
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• 2011

A department homepage contains various informations related with the department. So, a role of the department homepage is very important for both of students and applicants. For this reason, D university is evaluating the homepages of all departments in viewpoint of the construction, design, and contents of homepage. But the method of this homepage evaluation has some problems. Therefore, in this study, we propose a objective index for department homepage evaluation which can be applied to homepages of the other departments. Also we expect to provide an important information for the efficient homepage management by using the proposed index for homepage evaluation.

• The Journal of Information Systems
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• v.18 no.3
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• pp.351-373
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• 2009

An index fund is a collective investment scheme that aims to replicate the movements of an index of a specific financial market regardless of market conditions. An index fund is a popular investment alternative because it is much cheaper to run than an active fund and it performs better than actively managed funds. This paper illustrates the usefulness of wavelet analysis in constructing an index fund. The wavelet analysis can decompose the time series data in frequency domain as well as in time domain. The major findings of this paper are as follows. First, the beta coefficient that represents the systematic risk has the scale dependent property. This result can provide important information to the investors with various investment time frequency. Investors can use the betas corresponding to their investment frequencies among the various scale betas estimated by wavelet analysis. Second, we can find the usefulness of wavelet analysis in constructing index fund because the wavelet technique gives less tracking error(difference between the index performance and the index fund performance) than the traditional constructing techniques. The result of this study implies that the wavelet techniques can be an important analytic method to the other financial markets such as option market, futures market, bond markets and currency market.

• The Journal of Information Systems
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• v.28 no.3
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• pp.141-158
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• 2019

Purpose Artificial Intelligence (AI) is a core technology, leading the 4th industrial revolution. This study aims to diagnose the Korean's national competitiveness for AI technologies through patent analyses. Design/methodology/approach In this study, KIWEE and Derwent Innovation databases were used as data source of patents. we extracted 10,510 AI patents data with keywords and classified them into 15 subcategories of AI technology. We executed patent analyses for activity index, patent intensity index, technology strength, and patent family size and diagnosed Korea's national competitiveness in AI industry. Findings The results showed that Korea is less competitive than the United States and Japan in AI industry. However, patent amount has increased since 2010, which is encouraging result. This study has implication on the need for human and R&D investment in AI industry.

• Journal of Information Technology Services
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• v.13 no.2
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• pp.151-161
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• 2014

KOSPI 200 index options market has the highest trading volume in the global options markets. The risk and return structure of options contracts are very complex. Volatility complicates options trading because volatility plays a central role in options pricing process. This study develops a trading system for KOSPI 200 index options trading using KOSPI 200 volatility index. We design a database system to handle the complex options information such as price, volume, maturity, strike price, and volatility using Oracle DBMS. We then develop options trading strategies to test how the volatility index is related to the prices of complicated options trading strategies. Back test procedure is presented with PL/SQL of Oracle DBMS. We simulate the suggested trading system using historical data set of KOSPI 200 index options from December 2008 to April 2012.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.2
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• pp.157-166
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• 2004

Recently, switchboards for MV/LV distribution customers have been united and digitalized rapidly. This paper suggestes the effective information visualization method for the data mesured from cubicle switchboard for MV/LV distribution customers. We developed the algorithm that analyzes a large number of mesured data and displays them to users overall, such as fire index, power condition index, switchboard safety index, and power diminution index. Using a touch screen made users to operate it easily. User interface was also strengthened by taking graph visualization. We guess the information visualiztion method suggested in this paper shows the new direction that heavy electrical equipments including switchboards are going to develop in the future.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.37-50
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• 2021

Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.

• Journal of Information Technology Applications and Management
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• v.11 no.1
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• pp.101-115
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• 2004

The goal of this research is to improve SLA index that measures service performance level as a result of outsourcing Information system, and also to identify the best application method. This research identifies SLA applied cases and improving points based on the evidence collected from three major financial institutions that utilized outsourcing. First this study analyzes SLA applied cases of two different companies and discusses SLA application improvement possibilities. It then introduces a successful case of a third company that adopted SLA index.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.191-194
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• 2001

The availability of the inexpensive, large main memories coupled with the demand for faster response time are bringing a new perspective to database technology. The Web GIS used by u unspecified number of general public in the internet needs high speed response time and frequent data retrieval for spatial analysis rather than data update. Therefore, it is appropriate to use main memory as a underlying storage structures for the Web GIS data. In this paper, we propose a data representation method based on relative coordinates and the size of the MBR. The method is able to compress the spatial data widely used in the Web GIS into smaller volume of memory. We also propose a memory resident spatial index with simple mechanism for processing point and region queries. The performance test shows that the index is suitable for managing the skewed data in terms of the size of the index and the number of the MBR intersection check operations.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.837-847
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• 2012

Inverse Gaussian distribution is widely used in applications to analyze and model right-skewed data. To assess the appropriateness of the distribution prior to data analysis, Mudholkar and Tian (2002) proposed an entropy-based test of fit. The test is based on the entropy power fraction(EPF) index suggested by Gokhale (1983). The simulation results report that the power of the entropy-based test is superior compared to other goodness-of-fit tests; however, this observation is based on the small-scale simulation results on the standard exponential, Weibull W(1; 2) and lognormal LN(0:5; 1) distributions. A large-scale simulation should be performed against various alternative distributions to evaluate the power of the entropy-based test; however, the use of a theoretical method is more effective to investigate the powers. In this paper, utilizing the information discrimination(ID) index defined by Ehsan et al. (1995) as a mathematical tool, we scrutinize the power of the entropy-based test. The selected alternative distributions are the gamma, Weibull and lognormal distributions, which are widely used in data analysis as an alternative to inverse Gaussian distribution. The study results are provided and an illustrative example is analyzed.