• Asia pacific journal of information systems
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• v.24 no.1
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• pp.93-112
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• 2014

As personal data breach reared up as a problem domestically and globally, organizations appointing chief privacy officers (CPOs) are increasing. Related Korean laws, 'Personal Data Protection Act' and 'the Act on Promotion of Information and Communication Network Utilization and Information Protection, etc.' require personal data processing organizations to appoint CPOs. Research on the characteristics and role of CPO is called for because of the importance of CPO being emphasized. There are many researches on top management's role and their impact on organizational performance using the Upper Echelon theory. This study investigates what influence the characteristics of CPO gives on the organizational privacy performance. CPO's definition varies depending on industry, organization size, required responsibility and power. This study defines CPO as 'a person who takes responsibility for all the duties on handling the organization's privacy,' This research assumes that CPO characteristics such as role, personality and background knowledge have an influence on the organizational privacy performance. This study applies the part relevant to the upper echelon's characteristics and performance of the executives (CEOs, CIOs etc.) for CPO. First, following Mintzberg and other managerial role classification, information, strategic, and diplomacy roles are defined as the role of CPO. Second, the "Big Five" taxonomy on individual's personality was suggested in 1990. Among these five personalities, extraversion and conscientiousness are drawn as the personality characteristics of CPO. Third, advance study suggests complex knowledge of technology, law and business is necessary for CPO. Technical, legal, and business background knowledge are drawn as the background knowledge of CPO. To test this model empirically, 120 samples of data collected from CPOs of domestic organizations are used. Factor analysis is carried out and convergent validity and discriminant validity were verified using SPSS and Smart PLS, and the causal relationships between the CPO's role, personality, background knowledge and the organizational privacy performance are analyzed as well. The result of the analysis shows that CPO's diplomacy role and strategic role have significant impacts on organizational privacy performance. This reveals that CPO's active communication with other organizations is needed. Differentiated privacy policy or strategy of organizations is also important. Legal background knowledge and technical background knowledge were also found to be significant determinants to organizational privacy performance. In addition, CPOs conscientiousness has a positive impact on organizational privacy performance. The practical implication of this study is as follows: First, the research can be a yardstick for judgment when companies select CPOs and vest authority in them. Second, not only companies but also CPOs can judge what ability they should concentrate on for development of their career relevant to their job through results of this research. Cultural social value, citizen's consensus on the right to privacy, expected CPO's role will change in process of time. In future study, long-term time-series analysis based research can reveal these changes and can also offer practical implications for government and private organization's policy making on information privacy.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.237-246
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• 2020

this paper, a data modeling method based on decision-making conditions is proposed for making combat and battlefield management systems to be intelligent, which are also a decision-making support system. A picture of a robot seeing and perceiving like humans and arriving a point it wanted can be understood and be felt in body. However, we can't find an example of implementing a decision-making which is the most important element in human cognitive action. Although the agent arrives at a designated office instead of human, it doesn't support a decision of whether raising the market price is appropriate or doing a counter-attack is smart. After we reviewed a current situation and problem in control & command of military, in order to collect a big data for making a machine staff's advice to be possible, we propose a data modeling prototype based on decision-making conditions as a method to change a current control & command system. In addition, a decision-making tree method is applied as an example of the decision making that the reformed control & command system equipped with the proposed data modeling will do. This paper can contribute in giving us an insight of how a future AI decision-making staff approaches to us.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.151-161
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• 2016

As part of a policy to address climate change and pollution problem, the government introduced a green credit card scheme in order to motivate pro-environmental behaviors in July 2011. It is important to present the specific ways to facilitate pro-environmental behaviors using the consumer behavior pattern data. This study was a result of data from total fifty seven thousands customer purchasing history data of green credit card to be created for the 3 months from January to March 2015. As the analysis process is put in to operation the analysis of the purchasing customer's profile firstly, and the second come into association analysis to consider the buying associations for green products purchasing networks, the third estimate the useful parameters to affect the customer's pro-environmental behavior and customer characteristics. It shows that royal customers are from 30 to 40 years old and their incomes are from 30 million won to 40 million won. Especially, they live in Daegu, Gyeonggi, and Seoul.

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.40-45
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• 2018

With the advent of big data and social networks, large-scale graph processing becomes popular research topic. Recently, an optimization technique called Gorder has been proposed to improve the performance of in-memory graph processing. This technique improves performance by optimizing the graph layout on memory to have better cache locality. However, since it is designed for in-memory graph processing systems, the technique is not suitable for disk-based graph engines; also the cost for applying the technique is significantly high. To solve the problem, we propose a new graph ordering called I/O Order. I/O Order considers the characteristics of I/O accesses for SSDs and HDDs to improve the performance of disk-based graph engine. In addition, the algorithmic complexity of I/O Order is simple compared to Gorder, hence it is cheaper to apply I/O Ordering. I/O order reduces the cost of pre-processing up to 9.6 times compared to that of Gorder's, still its performance is 2 times higher compared to the Random in low-locality graph algorithms.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.605-613
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• 2015

This paper addresses the problem to make sparse the projection matrix in pattern recognition method. Recently, the size of computer program is often restricted in embedded systems. It is very often that developed programs include some constant data. For example, many pattern recognition programs use the projection matrix for dimension reduction. To improve the recognition performance, very high dimensional feature vectors are often extracted. In this case, the projection matrix can be very big. Recently, RSR(roated sparse regression) method[1] was proposed. This method has been proved one of the best algorithm that obtains the sparse matrix. We propose three methods to improve the RSR; outlier removal, sampling and elastic net RSR(E-RSR) in which the penalty term in RSR optimization function is replaced by that of the elastic net regression. The experimental results show that the proposed methods are very effective and improve the sparsity rate dramatically without sacrificing the recognition rate compared to the original RSR method.

• Journal of The Korean Astronomical Society
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• v.36 no.spc1
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• pp.75-81
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• 2003

Changes in the earth's climate depend on changes in the net sunlight reaching us. The net depends on the sun's output and earth's reflectance, or albedo. Here we develop the limits on the changes in the sun's output in historical times based on the physics of the origin of solar cycle changes. Many have suggested that the sun's output could have been $0.5\%$ less during the Maunder minimum, whereas the variation over the solar cycle is only about $0.1\%$. The frequencies of solar oscillations (f- and p-modes) evolve through the solar cycle, and provide the most exact measure of the cycle-dependent changes in the sun. But precisely what are they probing? The changes in the sun's output, structure and oscillation frequencies are driven by some combination of changes in the magnetic field, thermal structure and velocity field. It has been unclear what is the precise combination of the three. One way or another, this thorny issue rests on an understanding of the response of the solar structure to increased magnetic field, but this is complicated. Thus, we do not understand the origin of the sun's irradiance increase with increasing magnetic activity. Until recently, it seemed that an unphysically large magnetic field change was required to account for the frequency evolution during the cycle. However, the problem seems to have been solved (Dziembowski, Goode & Schou 2001) using f-mode data on size variations of the sun. From this and the work of Dziembowski & Goode (2003), we suggest that in historical times the sun couldn't be much dimmer than it is at activity minimum.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.357-371
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• 2018

The purpose of this study is to provide basic data on the improvement and development of Korean marathon through an analysis of the records of Korean Marathon and the World Marathon. Based on the records of the Donga Marathon, the Chuncheon Marathon, and the JoongAng Marathon, which are the three major marathons of Korea, the results of comparison and analysis of the records of Korean Marathon and World Marathon are as follows. First, it is rare for the host country to win. The problem is that, recently, for more than 10 years, the record gap with foreign players is increasing. Second, Comparisons between Olympic men and women and world championships men and women had equally competed for a winning record until the 1990s, but have not been able to reach the past winning record they recorded 20 years ago, and have been keeping a big difference in the record with the world record.

• Journal of Korea Game Society
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• v.18 no.2
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• pp.121-130
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• 2018

Most of those studies use frequency, the number of times a pattern appears in a transaction database, as the key measure for pattern interestingness. It prerequisites that any interesting pattern should occupy a maximum portion of the transactions it appears. But in our real world scenarios the completeness of any pattern is more likely to become various in transactions. Hence, we should also consider the problem of finding the qualified patterns with the significant values of the weighted support by completeness in order to reduce the loss of information within any pattern in transaction. In these pattern recommendation applications, patterns with higher completeness may lead to higher recall while patterns with higher completeness may lead to higher recall while patterns with higher frequency lead to higher precision. In this paper, we propose a measure of weighted support and completeness and an algorithm WSCFPM(weigted support and completeness frequent pattern mining). Our algorithm handles the invalidation of the monotone or anti-monotone property which does not hold on completeness. Extensive performance analysis show that our algorithm is very efficient and scalable for word pattern mining.

• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.595-604
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• 2003

A Digital TV system is necessary for data Processing as well as video and audio processing. Especially in the case of interactive broadcasting, it should manage returning channel created by the Internet, PSTN, and so on. Because of many functionalities and multitasking jobs, it needs an Operating System. Embedded Linux as open source program can increase a cost effectiveness in market and has many advantages - reusable device drivers and application programs, more convenient developing environment using shell and file system, and easy problem resolution within Open Source Community. In this paper, we modified Embedded Linux kernel and cross developing environment for a big-endian system, redesigned devices for kernel execution, and configured system memory map in order to load a linux kernel. Also we developed an device driver for entire system control.

• Knowledge Management Research
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• v.19 no.2
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• pp.151-162
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• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.