• 경영과학
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• 제14권1호
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• pp.87-105
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• 1997

There have been lots of research on temporal data management for the past two decades. Most of them are based on some logical data model, especially on the relational data model, although there are some conceptual data models which are independent of logical data models. Also, many properties or issues regarding temporal data models and temporal query languages have been studied. But some of them were shown to be incompatible, which means there could not be a complete temporal data model, satisfying all the desired properties at the same time. Many modeling issues discussed in the papers, do not have to be done so, if they take object-oriented data model as a base model. Therefore, this paper proposes a temporal data model, which is based on the object-oriented data model, mainly discussing the most essential issues that are common to many temporal data models. Our new temporal data model and query language will be illustrated with a small database, created by a set of sample transaction.

• 한국경영과학회지
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• 제14권1호
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• pp.87-87
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• 1989

There have been lots of research on temporal data management for the past two decades. Most of them are based on some logical data model, especially on the relational data model, although there are some conceptual data models which are independent of logical data models. Also, many properties or issues regarding temporal data models and temporal query languages have been studied. But some of them were shown to be incompatible, which means there could not be a complete temporal data model, satisfying all the desired properties at the same time. Many modeling issues discussed in the papers, do not have to be done so, if they take object-oriented data model as a base model. Therefore, this paper proposes a temporal data model, which is based on the object-oriented data model, mainly discussing the most essential issues that are common to many temporal data models. Our new temporal data model and query language will be illustrated with a small database, created by a set of sample transaction.

• 지능정보연구
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• 제3권2호
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• pp.33-55
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• 1997

A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.

• 한국멀티미디어학회논문지
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• 제18권4호
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• 한국대기환경학회지
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• 제18권6호
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• pp.437-452
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• 2002

The Metropolitan Tracer Experiment (METREX) was performed over the Washington, D.C. area using two inert, non-deposition perfluorocarbon gases for over 1 year period (November 1983∼December 1984). Two perfluorocarbon gas tracers (PDCH, PMCH) were released simultaneously at intervals of every 36 hours for 6 hours, regardless of the meteorological conditions in metropolitan area. Samples were collected continuously for 8 hours at a central downtown and two adjacent suburban locations. Monthly air samples were collected at 93 sites across the whole region (at urban, suburban, and rural locations). The purpose of this study is to simulate INPUFF and ISCST model using METREX data, and to compare calculated and observed concentrations. In the case of INPUFF simulation, two meteorological input data were used. One is result data from wind field model which was calculated by diagnostic wind model (DWM), the other is meteorological data observed at single station. Here, three kinds of model calculation were performed during April and July 1984; they include (1) INPUFF model using DWM data (2) INPUFF model using single meteorological data (3) ISCST model. The monthly average concentration data were used for statistic analysis and to draw their horizontal distribution patterns. Eight-hour-averaged concentration was used to describe movement of puff during the episode period. The results showed that the concentrations calculated by puff model (INPUFF) were better than plume model (ISCST). In the case of puff model (INPUFF), a model run using wind field data produced better results than that derived by single meteorological data.

• Nuclear Engineering and Technology
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• 제53권3호
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• pp.860-865
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• 2021

A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.

• 한국군사과학기술학회지
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• 제15권2호
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• pp.147-154
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• 2012

In this paper, we revise the JDL data fusion model to have an ability of distributed data fusion(DDF). Data fusion is a function that produces valuable information using data from multiple sources. After the network centric warfare concept was introduced, the data fusion was required to be expanded to DDF. We identify the data transfer and control between nodes is the core function of DDF. The previous data fusion models can not be used for DDF because they don't include that function. Therefore, we revise the previous JDL data fusion model by adding the core function of DDF and propose this new model as a model for DDF. We show that our model is adequate and useful for DDF by using several examples.

• 전기전자학회논문지
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• 제7권2호
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• pp.144-155
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• 2003

본 논문에서는 객체 기술과 복잡한 구조적 데이터와 관련된 데이터베이스 구조를 효율적으로 표현하기 위한 통합 데이타 모델(UDM)이라고 하는 개념 데이터 모델을 제안한다. 이 모델은 E-R 모델, 의미 객체 모델, UML 등의 데이타 모델의 주요 기능을 데이타베이스 설계 관점에서 통합한 것이다. 이 모델은 시공간 멀티미디어 데이타의 복잡한 구조를 모델링하기 위해 제안된 객체-관계 모델을 단순화시키고 일부 기능을 확장시킨 버전이다. 이 모델은 현대적인 데이터베이스 응용의 주요한 의미적, 구조적인 정보의 표현을 지원하며, 관계, 객체-관계, 객체-지향, (반-)구조 데이터베이스 등 주요 논리적 데이터베이스 모델을 지원하도록 고안되었다. 데이터베이스 설계를 위한 도구로 통합 데이타 다이아그램(UDD)을 제안하며, 이 통합 데이타 모델로부터 논리적 뷰를 유도하기 위한 방법도 제안한다. 제안된 모델은 다양한 개념적 데이터베이스 설계를 위한 편리하고 실용적인 도구로 활용될 수 있다.

• Journal of Information Technology Applications and Management
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• 제10권1호
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• pp.111-125
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• 2003

This paper proposes a product data model that can express and enforce integrity constraints on product structure during engineering changes (ECs). The model adopts and extends an active object-oriented database model in order to Integrate IC data and their integrity constraints. Tightly integrated with product structure, It will enable designers to maintain and exchange consistent EC data throughout the product life cycle. In order to properly support operations for ECs, the model provides the data, operations, and Event-Condition-Action rules for nested ECs and simultaneous EC applications to multiple options. in addition, the EC objects proposed In the model integrate the data and Integrity constraints into a unified repository. This repository enables designers to access all EC data and integrity constraints through the product structure and relationships between EC objects. This paper also describes a prototype product data management system based on the proposed model In order to demonstrate its effectiveness.

• ETRI Journal
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• 제28권2호
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• pp.191-204
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• 2006

Many previous studies of data quality have focused on the realization and evaluation of both data value quality and data service quality. These studies revealed that poor data value quality and poor data service quality were caused by poor data structure. In this study we focus on metadata management, namely, data structure quality and introduce the data quality management maturity model as a preferred maturity model. We empirically show that data quality improves as data management matures.