• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.66
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• pp.191-218
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• 2015

This research is, first of all, a theoretical study concerning 'conflict.' Only then, we could obtain ways in which we manage and resolve various problems arising from conflicts in business between Korean and Chinese companies. In doing this, we also tried to grasp cultural characteristics, or factors, in Chinese ways of carrying out negotiations that lead to conflicts with us. On the basis of these preliminary considerations, we developed practical techniques of conflict management and types of negotiation strategy. We thereby could suggest broader strategic implications for better performance in international business. Concretely, this research investigates techniques of conflict management and types of negotiation strategy. For such techniques and types, we suggest, (1) Sharing technique or reconciliatory compromising negotiation and its compromise strategy, (2) collaborative technique or cooperative negotiation and its win-win strategy, (3) competitive technique or competitive negotiation and its profit-seeking attack strategy, (4) accommodative technique or receptive negotiation and its relation-maintaining yield strategy, (5) avoidant technique or evasive negotiation and its indifference-showing avoidance strategy. This research contributes to promote understanding on negotiation culture of chinese corporate. and we provide the guideline of the conflict management and the insight for the efficiency strategy of chinese business negotiation. But, empirical data and statistical examinations should be added to our present research for the future prospective ones.

• Journal of Intelligence and Information Systems
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• v.3 no.1
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• pp.31-45
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• 1997

To date, research on data mining has focused primarily on individual techniques to su, pp.rt knowledge discovery. However, the integration of elementary learning techniques offers a promising strategy for challenging a, pp.ications such as forecasting nonlinear processes. This paper explores the utility of an integrated a, pp.oach which utilizes a second-order learning process. The a, pp.oach is compared against individual techniques relating to a neural network, case based reasoning, and induction. In the interest of concreteness, the concepts are presented through a case study involving the prediction of network traffic for video-on-demand.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4145-4162
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• 2021

During the period of epidemic prevention and control, contact tracing systems are developed in many countries, to stop or slow down the progression of COVID-19 contamination. However, the privacy issues involved in the use of contact tracing apps have also attracted people's attention. First, we divide contact tracing techniques into two types: Bluetooth Low Energy (BLE) based and Global Positioning System (GPS) based techniques. In order to clear understand the system structure and its elements, we create data flow diagram (DFD) of each types. Second, we analyze the possible privacy threats contained in various types of contact tracing apps by applying LINDDUN, which is a threat modeling technique for personal information protection. Third, we make a comparison and analysis of various contact tracing techniques from privacy point of view. These studies can facilitate improve tracing and security performance to contact tracing apps through comparisons between different types.

• Journal of Information Technology Application
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• v.3 no.2
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• pp.1-24
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• 2001

This purpose of this study is to drive important criteria for improving customer relationship of R institute using data mining techniques. The focus of this research is to consider patterns and interactions of research variables from research management database of R institute, and to classify the outside organizations and the inside organizations for research contract organizations, and to decide the directions of customer relationship management through analyzing the research type and research cost of research topics. In order to drive criteria variables through pattern analysis of the research database, decision tree algorithm is employed. The results show that determinant variables of 17 input variables are research period, overhead cost, R ＆ D cost as variables to classify the outside and inside contract organization.

• Journal of Environmental Impact Assessment
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• v.13 no.5
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• pp.231-241
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• 2004

This study aims to detect the change of marine aquaculture farm within the boundary of Hallyeo Marine National Park. Comparison has been made on the Landsat images taken in 1984 and 2002 respectively by using feature extraction methods and other image analysis techniques. During the 18 year period between 1984 and 2002, total area of the aquaculture farms has been decreased in 63 percent. The reason for the change seems to be that aquaculture farms became concentrated only around the Geoje Islands due to the growth of the labor- and capital-intensive cage aquaculture for the expensive fish species instead of traditional oyster farming. Authors suggest the monitoring using remotely-sensed data as the best tool for the management of marine aquaculture farms on the basis of accuracy of analysis and relatively cheap cost. Management strategies of salmon farms in Tasmania, Australia has been analyzed to find the field techniques necessary for the management of aquaculture.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.153-160
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• 2009

Efficient buffer management is closely related to system performance. Thus, much research has been performed on various buffer management techniques. However, many of the proposed techniques utilize the temporal locality of access patterns. In spatial database environments, there exists not only the temporal locality but also spatial locality, where the objects in the recently accessed regions will be accessed again in the near future. Thus, in this paper, we present a buffer management technique, called BEAT, which utilizes both the temporal locality and spatial locality in spatial database environments. The experimental results with real-life and synthetic data demonstrate the efficiency of BEAT.

• Asia pacific journal of information systems
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• v.12 no.1
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• pp.139-158
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• 2002

As the telecommunication market becomes mature in Korea, severe competition has already begun on the market. While service providers struggled for the last couple of years to acquire as many new customers as possible, nowadays they are making more efforts on retaining the current customers. The churn management by analyzing customers' demographic and transactional data becomes one of the key customer retention strategies which most companies pursue. However, the customer data analysis has still remained at the basic level in the industry, even though it has considerable potential as a tool for understanding customer behavior. This paper develops several churn prediction models using data mining techniques such as logistic regression, decision trees, and neural networks. For model-building, real data were used which were collected from one of the major telecommunication companies in Korea. This paper explores various ways of comparing model performance, while the hit ratio was mainly focused in the previous research. The comparison criteria used in this study include gain ratio, Kolmogorov-Smirnov statistics, distribution of the predicted values, and explanation ability. This paper also suggest some guidance for model selection in applying data mining techniques.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.1
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• pp.13-21
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• 2010

The prediction of contract cancellation of customers is essential in insurance companies but it is a difficult problem because the customer database is large and the target or cancelled customers are a small proportion of the database. This paper proposes a new data mining approach to the binary classification by handling a large-scale unbalanced data. Over-sampling, clustering, regularized logistic regression and boosting are also incorporated in the proposed approach. The proposed approach was applied to a real data set in the area of insurance and the results were compared with some other classification techniques.

• Asia pacific journal of information systems
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• v.27 no.2
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• pp.126-138
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• 2017

The proliferation of items increased the difficulty of customers in finding the specific items they want to purchase. To solve this problem, companies adopted recommender systems, such as collaborative filtering systems, to provide personalization services. However, companies use only meaningful and essential data given the explosive growth of data. Some customers are concerned that their private information may be exposed because CF systems necessarily deal with personal information. Based on these concerns, we analyze the effects of the amount of information on recommendation performance. We assume that a customer could choose to provide overall information or partial information. Experimental results indicate that customers who provided overall information generally demonstrated high performance, but differences exist according to the characteristics of products. Our study can provide companies with insights concerning the efficient utilization of data.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.159-173
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• 1998

Quality characteristics on the properties of process capability indices (PCIs) are often required to be normally distributed. But, if a characteristic is not normally distributed, serious errors can result from normal-based techniques. In this case, we may well consider the use of new PCIs specially designed to be robust for non-normality. In this paper, a newly proposed measure of process capability is introduced and compared with existing PCIs using the simulated non-normal data.