• 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.

• 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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.615-616
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• 2014

Currently, XML is a standard language used to exchange data. Most of the data in the file system is not stored in the database system. The data stored in an object-oriented database, the data can be represented by a hierarchical structure. However, in the case of a relational database table, each independently of the hierarchical structure data is present can not be expressed. In this paper, a hierarchical representation of data is difficult in traditional relational database without changing the data in the database, without having to build a new database, Define the structure of the existing data in the XML document for the automatic generation of a PL / SQL procedure is designed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.469-476
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• 2016

Relational Database Management Systems have become de facto database model among most developers and users since the inception of Data Science. From IoT devices, sensors, social media and other sources, data is generated in structured, semi-structured and unstructured formats, in huge volumes, thereby the difficulty of data management greatly increases. Organizations that collect large amounts of data are increasingly turning to non relational databases - NoSQL databases. In this paper, through experiments with real field data, we demonstrate that MongoDB, a document-based NoSQL database, is a better alternative for building a Telco Subscriber Data Management System which hitherto is mainly built with Relational Database Management Systems. We compare the existing system in various phases of data flow with our proposed system powered by MongoDB. We show how various workloads at some phases of the existing system were either completely removed or significantly simplified on the new system. Based on experiment results, using MongoDB for managing telco subscriber data turned out to offer performance better than the existing system built with MSSQL Server.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.197-214
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• 1996

Financial analysis of stock data usually involves extensive computation of large amount of time series data sets. To handle the large size of the data sets and complexity of the analyses, database management systems have been increasingly adaopted for efficient management of stock data. Specially, relational database management system is employed more widely due to its simplistic data management approach. However, the normalized two-dimensional tables and the structured query language of the relational system turn out to be less effective than expected in accommodating time series stock data as well as the various computational operations. This paper explores a new data management approach to stock data management on the basis of an object-oriented database management system (ODBMS), and proposes a data model supporting times series data storage and incorporating a set of financial analysis functions. In terms of functional stock data analysis, it specially focuses on a primitive set of operations such as variance of stock data. In accomplishing this, we first point out the problems of a relational approach to the management of stock data and show the strength of the ODBMS. We secondly propose an object model delineating the structural relationships among objects used in the stock data management and behavioral operations involved in the financial analysis. A prototype system is developed using a commercial ODBMS.

• 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 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.445-453
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• 2005

The design process of a relational database system consists of requirement analysis, conceptual design using ER diagram, logical design, and physical design. In logical design process, the conceptual schema is transformed to relational schema, and relational schema is normalized. This traditional design process is hard to applied in real database design process, since there is an ambiguity in conceptual design process. In this paper, we suggest a new design process, which provides more structural design steps by removing the conceptual design process. In new approach, we produce the data flow diagram by the structural methodology. From the attributes in the data store of data flow diagram, we construct relational table schema, and we normalize relational schema. Finally we produced table relationship diagram in order to figure out relationships between tables.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.405-414
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• 2010

A rule is defined as a case to determine the target values according to combination of various Business factors. The information system is used to represent enterprise's business, which includes and implements the amount of these rules to Rule-Based System. A Rule-Based System can be constructed by using the rules engine method or Relational Database technology. Because the rules engine method has some disadvantages, the Rule-Based System is mostly developed with Relational Database technology. When business scales become larger and more complex, a large number of various rule cases must be operated in system, and processing these rule cases requires additional time, overhead and storage space, and the speed of execution slows down. To solve these problems, we propose a simplification algorithm that converts a large amount of rule cases to simplification rule cases with same effects. The proposed algorithm is applied to hypothetical business rule data and a large number of simplification experiments and tests are conducted. The final results proved that the number of rows can be reduced to some extent. The proposed algorithm can be used to simplify business rule data for improving performance of the Rule-Based System implemented with the Relational Database.