• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.2
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• pp.109-122
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• 2003

As XML documents become popular in the World Wide Web, a lot of research is being done on XML storage systems that store and manage XML documents using existing DBMSS. However, most of them have been done in the context of relational DBMSS rather than object-relational DBMSS, which have more powerful modeling capability than relational ones. In this paper, we design and implement an XML storage system, ODYSSEUS/XMLStore, for the ODYSSEUS object-relational DBMS that has been under development at KAIST. First, we analyze the mapping from the structure of XML documents to the relational or object-relational database schema. Second, we propose a method for describing the mapping analyzed using a standard language, XML Schema. Third, we propose a storage structure for storing the mapping information specified by the users in the database. Fourth, we propose detailed algorithms for storing XML documents in the relational or object-relational database based on the mapping information provided by the users.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.49-52
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• 2003

In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert systems. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently, and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.533-538
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• 2001

In this paper, we extend FARGs single mode attribute to multiple attributes for real image application and present a new CBIR using FMARG(Fuzzy Multiple Attribute Relational Graph), which can handle queries involving multiple attributes, not only object label, but also color, texture and spatial relation. In the experiment using the synthetic image database of 1,024 images and the natural image database of 1.026 images built from NETRA database and Corel Draw, the proposed approach shows 6~30% recall increase in the synthetic image database and a good performance, at the displacements and the retrieved number of similar images in the natural image database, compared with the single attribute approach.

• The Journal of Society for e-Business Studies
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• v.8 no.1
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• pp.103-111
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• 2003

Nowadays XML is used for exchanging information in e-Commerce, especially B2B. Necessity of XML DBMS has being increased to efficiently process XML data. So a lots of database products for supporting XML are rapidly appeared in the market. In this paper, we made an XML DBMS system based on Generic Data Model. First we developed XML Adaptor based on Generic Data Model and added it on relational DBMS for developing XML DBMS. XML Adaptor is composed of Query Convertor and XML Repository System. The Query Convertor parse commands that are for XML data manipulation and then call the relevant component of XML Repository System for relational database operation. The XML Repository System handles relational database operations such as create, delete, store, and etc. In this way we can use a relational DBMS for manipulation XML data. Therefor we can build more economically XML DBMS.

• Journal of the Korea Society for Simulation
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• v.11 no.2
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• pp.55-66
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• 2002

VHDL has been. widely used in modeling and simulation of hardware designs. However, complex relationship between components of the designs makes the VHDL modeling problem very difficult. Furthermore, after the initial creation of VHDL models, they evolve into many versions over their lifetime. To cope with such difficulties, this paper proposes a new methodology for the management of VHDL models supporting versions. Its conceptual bases are system entity structure and relational database. Within the methodology, a family of hierarchical structures of a design is organized in the form of VHDL model structure. It is, in turn, represented in the form of relational tables. Once the model structure is built in such a way, a specific simulation model which meets design objective is pruned from the model structure. The details of VHDL codes are systematically synthesized by combining it with the primitive models in a model base. These algorithms are also defined in terms of relational algebraic operations.

• Journal of KIISE
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• v.43 no.12
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• pp.1385-1395
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• 2016

In the era of Big Data, NoSQL databases provide solutions for problems, circumventing the limitations of traditional relational databases by using new architectures and data model. Contrary to relational database products, the range of the features architectures, and limitations of NoSQL databases is very broad. Thus, choosing the right database products requires more considerations and difficulties. The advent of NoSQL does not only promote the abundance of NoSQL products, but also stimulates the relational database realm to expand their features beyond the relational model. In order to understand NoSQL trends more accurately, here we discuss and compare NoSQL databases with relational databases. We also present the newest features associated with NoSQL in one of the most advanced open-source relational databases, PostgreSQL. To discuss future directions for PostgreSQL we analyzed the performance of NoSQL and PostgreSQL by conducting experiments using the NoSQL benchmark tool (YCSB).

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.126-132
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• 2004

The database design for the management of bridge measurement information is presented in this paper. To express the associated data generated during the whole process of ambient measurement efficiently, requirements analysis for database construction is performed. And to define objects and organize schema conceptual and logical design are performed, which convert data model into logical schema. Finally, physical design is performed using DDL(data defined language). This database is based on the object-relational data modeling approach that has rich expressive power and good reusability in comparison with the traditional entity-relational modeling.

• Journal of the Korea Computer Industry Society
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• v.2 no.2
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• pp.123-130
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• 2001

As the growth of database volume, it has required a need and an opportunity of data analysis and extracting knowledge from database. Data mining method is the representative example. The size of most minable data set is huge, and stored in a database. To implement effective mining function, we must extract minable data set to be analyzed from existing relational database, and it must be managed with its generalized information. In this paper, the new mining operator is defined in a similar manner to the existing SQL operators and SQL is extended to extract data subset from relations and to generalize it using domain-oriented method. The background knowledge includes attribute values, which will be mind and generalized information, and it is managed as the same structure with a relation in relational database. These functions are implemented by defining some SQL - like operators and aggregated functions, and we describe the expressive powers of these new operators and functions through examples.