• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1046-1050
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• 2010

Corpus is widely used as a fundamental resource for various purposes in linguistic studies. There are several large corpora such as Sejong corpus in Korea. However, it is hard to find a tool utilizing such large corpora. In this paper, we propose a method of utilizing Sejong corpus based on the relational database. We designed the relational database scheme to store corpus and implemented a Web-based application so that many researchers can easily access and utilize the Sejong corpus.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.128-133
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• 1998

This paper aims to present some ideas for implementation of Uncertain Relational Databases (URD) which are extensions of classical relational databases. Our system firstly is based on possibility distribution and probability theory to represent and manipulate fuzzy and probabilistic information, secondly adopts flexible mechanisms that allow the management of uncertain data through the resources provided by both available relational database management systems and front-end interfaces, and lastly chooses dynamic SQL to enhance versatility and adjustability of systems.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1346-1351
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• 2009

In this paper we propose a framework for an Object Relational IFC Server (OR-IFC Server). Enormous amounts of information are generated in each project. Today, many BIM systems are developed by various CAD software vendors. Industry Foundation Classes (IFC) developed by International Alliance for Interoperability (IAI) is an open standard data model for exchanging data between the various BIM tools. The IFC provides a foundation for exchanging and sharing of information directly between software applications and define a shared building project model. The IFC model server is a database management system that can keep track of transactions, modifications, and deletions. It plays a role as an information hub for storing and sharing information between various parties involved in construction projects. Users can communicate with each other via the internet and utilize functions implemented in the model server such as partial data import/export, file merge, version control, etc. IFC model servers using relational database systems have been developed. However, they suffered from slow performance and long transaction time due to a complex mapping process between the IFC structure and a relational-database structure because the IFC model schema is defined in the EXPRESS language which is object-favored language. In order to simplify the mapping process, we developed a set of rules to map the IFC model to an object-relational database (ORDB). Once the database has been configured, only those pieces of information that are required for a specific information-exchange scenario are extracted using the pre-defined information delivery manual (IDM). Therefore, file sizes will be reduced when exchanging data, meaning that files can now be effectively exchanged and shared. In this study, the framework of the IFC server using ORDB and IDM and the method to develop it will be examined.

• Proceedings of the Korean Geotechical Society Conference
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• 1998.10a
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• pp.105-110
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• 1998

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.73-73
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• 1988

Knapsack problems represent many business application such as cargo loading, project selection, and capital budgeting. In this research we developed a knapsack problem solver based on Martello-Toth algorithm using a relational database management system on the PC platform. The solver used the menu-driven user interface. The solver can be easily integrated with the database of decision support system because the solver can access the database to retrieve the data for the model and to store the result directly.

• Korean Management Science Review
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• v.13 no.2
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• pp.73-94
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• 1996

Knapsack problems represent many business application such as cargo loading, project selection, and capital budgeting. In this research we developed a knapsack problem solver based on Martello-Toth algorithm using a relational database management system on the PC platform. The solver used the menu-driven user interface. The solver can be easily integrated with the database of decision support system because the solver can access the database to retrieve the data for the model and to store the result directly.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.169-177
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• 2012

In the development process of application systems, the most important works are analysis and design. Most of the application systems are implemented on database system. So, database design is important. Also, IT System are confronted with more and more attacks by an increase interconnections between IT systems. Therefore security-related processes belong to a very important process. Security is a complex non-functional requirement that can interaction of many parts in the system. But Security is considered in the final stages of development. Therefore, Their increases the potential for the final product to contain vulnerabilities. Accordingly, Early in development related to security analysis and design process is very important. J2EE gives a solution based on RBAC((Role Based Access Control) for security and object-relational database also has RBAC for security. But there is not a object-oriented analysis and design methodology using RBAC of J2EE and object-relational database for security. In this paper, the unified object-oriented analysis and design methodology is developed for security-critical web application systems based on J2EE and object-relational database. We used UMLsec and RBAC of object-relational database and J2EE for this methodology.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.5
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• pp.257-265
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• 2004

The complex documentation involved in power system analysis software require a well-defined and friendly database system. We have developed an object-oriented database management system for power system analysis, and have described load flow analysis and transient stability analysis using object-oriented database(OODB). Database management systems are widely used and achieve high reliability of data management in the engineering fields. However relational database system have shortcomings in application to power system analysis. ill relational database, the data model is too simple for modeling complex data and database languages are very different from programming languages. Object-oriented techniques are sufficiently powerful to support data-modeling requirements of GUI applications. The GUI is implemented using C++ on a MS windows platform. The OODB supports data modeling requirements of GUI applications and the performance is well acceptable for Gill applications.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.1-7
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• 2011

Most of expert systems use the text-oriented knowledge bases. However, knowledge management using the knowledge bases is considered as a huge burden to the knowledge workers because it includes some troublesome works. It includes chasing and/or checking activities on Consistency, Redundancy, Circulation, and Refinement of the knowledge. In those cases, we consider that they could reduce the burdens by using relational database management systems-based knowledge management infrastructure and convert the knowledge into one of easy forms human can understand. Furthermore they could concentrate on the knowledge itself with the support of the systems. To meet the expectations, in this study, we have tried to develop a general-purposed knowledge conversion tool for expert systems. Especially, this study is focused on the knowledge conversions among text-oriented knowledge base, relational database knowledge base, and decision tree.