• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.726-734
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• 2015

Relational databases used by structuralizing data are the most widely used in data management at present. However, in relational databases, service becomes slower as the amount of data increases because of constraints in the reading and writing operations to save or query data. Furthermore, when a new task is added, the database grows and, consequently, requires additional infrastructure, such as parallel configuration of hardware, CPU, memory, and network, to support smooth operation. In this paper, in order to improve the web information services that are slowing down due to increase of data in the relational databases, we implemented a model to extract a large amount of data quickly and safely for users by processing Hadoop Distributed File System (HDFS) files after sending data to HDFSs and unifying and reconstructing the data. We implemented our model in a Web-based civil affairs system that stores image files, which is irregular data processing. Our proposed system's data processing was found to be 0.4 sec faster than that of a relational database system. Thus, we found that it is possible to support Web information services with a Hadoop-based big data processing technique in order to process a large amount of data, as in conventional relational databases. Furthermore, since Hadoop is open source, our model has the advantage of reducing software costs. The proposed system is expected to be used as a model for Web services that provide fast information processing for organizations that require efficient processing of big data because of the increase in the size of conventional relational databases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1222-1225
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• 1993

The purpose of this paper is to construct a prediction system on the chance of rain in a local region using a fuzzy relational model. The prediction system consists of two parts. One is a prediction part on the chance of rain. The compositional law of fuzzy inference, proposed by Zadeh, is applied to predict the chance of rain. The other is a learning part of a fuzzy relational model using input-output data. A simple and fast learning algorithm is used in this part. Simulations are carried out by the actual weather data in our city and their results show the validity of prediction by the fuzzy relational approach.

• Fourth Industrial Review
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• v.1 no.2
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• pp.1-8
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• 2021

Purpose - By suggesting relational distance between supervisor and subordinate, this study examines the boundary condition of the impact of transformational leadership and organizational commitment in Korea. Research design, data, and methodology - We collected survey data from employees in various industries in Korea. A total of 241 employees participated in this study. We conducted the hierarchical linear regression and confirmed moderating effects of relational distances (democratic, structural and affective distance) on the relationship between transformational leadership and organizational commitment. Result - The results from 241 Korean employees show that transformational leadership is positively related to organizational commitment. Further, this relationship was moderated by structural distance and affective distance between supervisor and subordinate. Conclusion - This study shed new light on how exercising transformational leadership can help raise employees' organizational commitment. Organizations should be concerned about the various relational distance between supervisor and subordinate.

• 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.

• 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 Internet Computing and Services
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• v.12 no.6
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• pp.149-159
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• 2011

The current applications can be characterized as consisting of complex objects related by complex relationship. Therefore the relational database has been extended to object-relational database because of the complex objects. Accordingly, we need database design methodology for object-relational database. In this pager, we develop an integrated design methodology based on a extended UML class diagram for object-relational database. We make the extend UML class diagram by adding new stereotypes for object-relational database. Also we propose a guideline for transforming the extended UML class diagram to object-relational database schema. We use SQL:1999 as a object-relational data model and Oracle11g as a target object-relational database. We can build more easily and efficiently object-relational database for Oracle11g by using our methodology. Finally we applied our methodology on a computer classroom reservation system for evaluation the methodology.

• Spatial Information Research
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• v.5 no.1
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• pp.77-88
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• 1997

This paper presents the design and implementaion of an object-relational geographic information system. This system has been developed on top of a commercial object-relational database management system. It provides flexible spatial data model, spatial query language, visual user interface, and efficient spatial access methods(D0T) in which traditional primary-key access methods can be applied. We report on our design choices and describe the current status of Implementation. The conceptual model of the system is based on SDTS, and is mapped to the intemal obiect-oriented data model. Kevwords : object-oriented data model, GIS, spatial data model, spatial access method.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.123-133
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• 2010

XML has been become the new standard for publishing and exchanging data on the Web. However, most business data is still stored and maintained in relational database management systems. As such, there is an increasing need to efficiently publish relational data as XML data for Internet-based applications. The most important issue in the transformation is to reflect structural and semantic relations of RDB to XML schema exactly. Most transformation approaches have been done to resolve the issue, but those methods have several problems. In this paper, we discuss algorithm in transforming a relational database schema into corresponding XML schema in XML Schema. We aim to achieve not only explicit/implicit referential integrity relation information but also high level of nested structure while introducing no data redundancy for the transformed XML schema. To achieve these goals, we propose a transformation model which is redundancy free and then we improve the XML Schema structure by exploring more nested structure.

• 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 Information and Commucation Sciences Conference
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• 2003.10a
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• pp.615-618
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• 2003

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.