• Management & Information Systems Review
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• v.31 no.3
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• pp.141-160
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• 2012

This study investigate the relationship between embeddedness of supply chain on supply chain performance. The development of research model is based on network embeddedness that the literature of strategic management and sociology. To examine the research model and hypotheses, we have used an empirical method based on field survey in which most of measurements used and verified in previous studies are selected as measurements. The data from survey was analyzed using Partial Least Squares(PLS). The result from empirical model suggest as follow; First, relational embeddedness of supply chain effects on supply chain performance. Especially, reciprocal dependance affects interfirm relation performance. Also trust and tie strength of relational embeddedness affects interfirm relation performance. Second, interfirm relation performance affects financial performance.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6B
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• pp.349-354
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• 2005

Several IP autoconfiguration techniques for MANET have been proposed by this time. But most of them concentrate on dealing with address duplication, so they have problems with latency and inefficient address space utilization. In this paper, we proposed a new IP autoconfiguration technique, namely Relational MANET Autoconfiguration Protocol(RMAP). This technique distributes IP addresses effectively by exchanging messages and recovers IP addresses by applying parent-child relationship. Simulation experiments are conducted to demonstrate the enhancement of the proposed algorithm over other known algorithms regarding latency, uniform distribution, and IP address recovery ratio.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.348-356
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• 2012

This study is identifying early takeoff phenomenon in relational network like SNS and investigates influence of innovator or hub at early takeoff and diffusion size at takeoff point. The major finding of this study are as follows. First, multinominal-logit analysis conducted to explore the impact of innovator and hub at early stage of information diffusion and early takeoff. Hub in early stage of information diffusion impact early takeoff. Second, our study shows that innovator and hub at early stage of information diffusion impact diffusion size at takeoff point. Third, hub is more impact early takeoff and diffusion size at takeoff point then innovator. This study provides implication for professionals by confirming hub's influence in study of successful information diffusion by early takeoff. The limitation and future direction are discussed.

• Knowledge Management Research
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• v.14 no.2
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• pp.71-88
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• 2013

The aim of this study was to investigate why people voluntarily contribute knowledge to others, primarily strangers, in the electronic network of practice for job examination expected potential competing. This paper is organized as follows. First, we introduce the electronic network of practice which is the knowledge sharing community for job examination, and discuss the key issues for understanding knowledge sharing in these networks on the basis of individual motivations, relational capital, sense of community, and sense of rivalry to develop a research model for this study. To test the proposed research model, we adopted the survey method for data collection, and examined our hypotheses by applying the multiple regression analysis method to the collected data. Our unit of analysis was the individual. The findings of this study show that the intention of knowledge sharing is influenced by the reputation and the enjoy helping as the factors of individual motivations, by the reciprocity as the factor of relational capital, and by a sense of rivalry as a psychological factor. Lastly, contributions of this study and future research opportunities are also discussed.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.305-319
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• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.138-149
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• 2019

The purpose of this study is to empirically address the influences of motives for using SNS on relational bridging and relational bonding in SNS and motives for continuous use SNS by applying the concepts of bonding and bridging, defined in the social capital theory, and major motives for using SNS are classified into three categories-social, information, and recreation categories - based on the previous studies and the usage and gratification theories. Through this study, it was revealed that motives of all these three categories-social, information, and recreation categories-have significant influences over relational bridging and relational bonding in SNS while relational bridging and relational bonding in SNS have significant influences over consistent motives for using SNS so the hypothesis of this study is accepted. It was also proven that motives of all these three categories - social, information, and recreation categories-for using SNS are important factors for creating new communities or expand relationships with other SNS users sharing similar interests. In addition, it was found that SNS users constantly use SNS to build relationships that could create a strong bond between individuals and receive emotional supports when necessary and to expand its own network by creating new relationships with other SNS users.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2178-2184
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• 2015

With the development of wireless network technologies, mobile users may use various networks easily and expect more advanced services. On the other hand, it may bring on some problems with network resource management that should lead the service provider to improve the current service quality and manage the network resource efficiently. This paper proposes the optimized radio resource management (RRM) scheme that integrates the Grey Relational Analysis (GRA) and game theory. The first applies the GRA to determine the Grey Relation Coefficient (GRC) factors that represent the network preference, and the network provider then selects the requested service that provide maximum payoff through Nash Equilibrium. Six requested services that have one application service among four different types of service classes were considered and the game was played repeatedly. In WiMAX, WLAN 1 and WLAN 2 game, the maximum payoff of each players was 93, 90.6 and 92.8 respectively. The experimental results show that every requested service can be selected by the network provider. Consequently, the proposed radio resource management mechanism is more effective in heterogeneous wireless networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.22-24
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• 2022

Recently, Graph Database (GDB) is being used in wide range of industrial fields. GDB is a database system which adopts graph structure for storing the information. GDB handles the information in the form of a graph which consists of vertices and edges. In contrast to the relational database system which requires pre-defined table schema, GDB doesn't need a pre-defined structure for storing data, allowing a very flexible way of thinking about and using the data. With GDB, we can handle a large volume of heavily interconnected data. A network service provider provides its services based on the heavily interconnected communication network facilities. In many cases, their information is hosted in relational database, where it is not easy to process a query that requires recursive graph traversal operation. In this study, we suggest a way to store an example set of interconnected network facilities in GDB, then show how to graph-query them efficiently.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.4
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• pp.21-28
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• 2001

In this study, GNLP(GIS linked non-linear network analysis program) for pipeline system analysis has been developed. This GNLP gets the input data for pipeline analysis from existing GIS(geographic information system) data automatically, and has GUI(graphic user interface) for user. Non-Linear Method was used for hydraulic analysis of pipe network based on Hazen-Williams equation, and Microsoft Access of relational database management system(RDBMS) was used for the framework of database applied program. GNLP system environment program was improved so that a pipe network designer can input information data for hydraulic analysis of pipeline system more easily than that of existing models. Furthermore this model generate output such as pressure and water quantities in the form of a table and a chart, and also produces output data in Excel file. This model is also able to display data effectively for analysed data confirmation and query function which is the core of GIS program.