• 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.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.27-39
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• 2012

As many applications are possible now in mobile environment with the trend of mobile convergence, diverse applications in healthcare industry are also possible in mobile devices. Though lots of researches on mobile and health services are introduced, they are limited to specific area or techniques. This study shows possible directions of fusion between mobile technologies and health services in the future using a data mining technique called association rule analysis. The data used in this study is collected from web pages containing key words related to mobile technologies and health services. The analysis shows that current cases of fusion between monitoring based telemedicine and patients. It also shows another case of fusion between mobile hospital and medical screen charts. These show that fusion between mobile technologies and health services already began in industry. Association rules are found between well-being, city, diet, and sleep. The association rules containing security and privacy, though their associations are not so strong, also show that security and privacy of patient information should be protected in the future. The results show that the fusion of mobile technologies and health services is expected to provide health services to more users and larger areas. It is also expected to create new diverse business models in the future.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.204-209
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• 2002

The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

• Journal of Fisheries and Marine Sciences Education
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• v.20 no.3
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• pp.416-428
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• 2008

In the end of 2007, Korea Government promulgated the Rule of 'Basic Act of Logistics Policy' for improving international logistics forwarding business. The goals of these rules are to achieve the development of our nation's economics for providing the security and efficiency of logistics system and enforced competition of logistics enterprises. This is established the basic principles of the legal basis for expanding into the Logistics Hub Center of North-east Asia. However In May 1999 new licensing requirements for ocean freight forwarders and NVOCCs operating in the USA were established by the US Federal Maritime Commission(FMC). Due to these regulations, each ocean transportation service provider in the USA acting as ocean freight forwarder, NVOCCs, or NVOCC agent must obtain a license to operate as Ocean Transportation Intermediary(OTI) before it begins operations. Only licensed OTIs may act as US transportation agents or receiving agents of other NVOCCs, on both US exports and imports. In this context, I think this study will be contributes for the development of korean freight forwarding system by analysis and comparing with between the Rule of the Basic Act of Logistics Policy of Korea and OTI freight forwarder & NVOCCs of USA.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.415-424
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• 2002

Semiconductor manufacturing has been emerged as a highly competitive but profitable business. Accordingly it becomes very important for semiconductor manufacturing companies to meet customer demands at the right time, in order to keep the leading edge in the world market. However, due-date oriented production is very difficult task because of the complex job flows with highly resource conflicts in fabrication shop called FAB. Due to its cyclic manufacturing feature of products, to be completed, a semiconductor product is processed repeatedly as many times as the number of the product manufacturing cycles in FAB, and FAB processes of individual manufacturing cycles are composed with similar but not identical unit processes. In this paper, we propose a production scheduling and control scheme that is designed specifically for semiconductor scheduling environment (FAB). The proposed scheme consists of three modules: simulation module, cycle due-date estimation module, and dispatching module. The fundamental idea of the scheduler is to introduce the due-date for each cycle of job, with which the complex job flows in FAB can be controlled through a simple scheduling rule such as the minimum slack rule, such that the customer due-dates are maximally satisfied. Through detailed simulation, the performance of a cycle due-date based scheduler has been verified.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.1
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• pp.80-90
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• 2010

Performance management is very popular in business area and seems to be an exciting topic. Despite significant research efforts and myriads of performance metrics, performance management today as a rigorous approach is still in an immature state and metrics are often selected based on intuitive and heuristic approach. In a R&D sector, the difficulty to select the proper performance metrics is even more increasing due to the natural characteristics of R&D such as unique or domain-specific problems. In this paper, we present a way of presenting R&D performance framework using ontology language. Based on this, the specific metrics can be derived by reusing or inheriting the context in the framework. The proposed ontological framework is formalized using OWL(Ontology Web Language) and metrics selection rules satisfying the characteristics of R&D are represented in SWRL(Semantic Web Rule Language). Actual metrics selection procedure is carried out using JESS rule engine, a plug-in to Prot$\acute{e}$g$\acute{e}$, and illustrated with an example, incorporating a prevalent R&D performance model : TVP(Technology Value Pyramid).

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.371-384
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• 2012

This study proposes a multi-level data analysis approach to identify both superficial and latent relationships among variables in the data set obtained from a vocational rehabilitation (VR) services program of people with significant disabilities. At the first layer, data mining and statistical predictive models are used to extract the superficial relationships between dependent and independent variables. To supplement the findings and relationships from the analysis at the first layer, association rule mining algorithms at the second layer are employed to extract additional sets of interesting associative relationships among variables. Finally, nonlinear nonparametric canonical correlation analysis (NLCCA) along with clustering algorithm is employed to identify latent nonlinear relationships. Experimental outputs validate the usefulness of the proposed approach. In particular, the identified latent relationship indicates that disability types (i.e., physical and mental) and severity (i.e., severe, most severe, not severe) have a significant impact on the levels of self-esteem and self-confidence of people with disabilities. The identified superficial and latent relationships can be used to train education program designers and policy developers to maximize the outcomes of VR training programs.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.99-104
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• 2003

In this research, we proposed 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 former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have 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, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. 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, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• Asia pacific journal of information systems
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• v.25 no.1
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• pp.83-97
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• 2015

Recently, the exhibition industry has developed rapidly with the development of information technologies. Most exhibitors in an exhibition plan and deploy many events that may provide advantages to visitors as a method of effective promotion. The growth and propagation of wireless technologies is a powerful marketing tool for exhibitors. However, exhibitors still rely on domain experts who are costly and time consuming because of the manual knowledge input procedure. Moreover, it is prone to biases and errors and not suitable for managing fast-growing and tremendous amounts of data that far exceed a human's ability to comprehend. To overcome these problems, data mining technology may be a great alternative, but it needs to be fit to each exhibition. This study uses data mining technology with the Predictive Model Markup Language (PMML) to suggest a system that supports intelligent services and that improves stakeholder satisfaction. This system provides advantages to the exhibitor, show organizer, and system designer, and is first enhanced by integrating data mining technologies through the knowledge of exhibition experts. Second, using the PMML, the system can automate the process of applying data mining models to solve real-time processing problems in the exhibition environment.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.69-76
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• 2008

In this thesis, we implemented the prototype system for the class code generator based on consistent code generation process and standard type, the class to be component unit. Particularly, we proposed relationship rule to solve the difficult problem by the object-oriented language to association and aggregation between classes based on component, through this method we can make to consistent code generation standard. Also it is adopted to component model construction which is generated code using code generation, and it can be basic assembly and deployment of business components to reusable target in developing application system.