• 한국경영과학회지
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• 제21권3호
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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.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.338-345
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• 2023

Nowadays, many companies are striving to turn customer value into business value. Customer Relationship Management is a management system that develops effective and efficient marketing strategies by classifying customers in detail based on their information, i.e. databases, and consists of various information technologies. To implement this management system, a customer integration database must be established, and customer characteristics (buying behavior, preferences, etc.) must be analyzed with the databases established and the behavior of each customer must be predicted. This study aims to systematically manage a large amount of customer data generated by companies that apply Customer Relationship Management, in order to develop data design and data governance strategies that should be considered to increase customer value and even company value. We mainly looked at the characteristics of customer relationship management and data governance, and then explored the link between the field of customer relationship management and data governance. In addition, we have developed a data strategy that companies need to perform data governance for customer relationship management.

• 정보관리학회지
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• 제40권1호
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• pp.51-71
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• 2023

본 연구는 공공데이터 품질관리 모델, 빅데이터 품질관리 모델, 그리고 연구데이터 관리를 위한 데이터 생애주기 모델을 분석하여 각 품질관리 모델에서 공통적으로 나타나는 구성 요인을 분석하였다. 품질관리 모델은 품질관리를 수행하는 객체인 대상 데이터의 특성에 따라 생애주기에 맞추어 혹은 PDCA 모델을 바탕으로 구축되고 제안되는데 공통적으로 계획, 수집 및 구축, 운영 및 활용, 보존 및 폐기의 구성요소가 포함된다. 이를 바탕으로 본 연구는 연구데이터를 대상으로 한 품질관리 프로세스 모델을 제안하였는데, 특히 연구데이터를 대상 데이터로 하여 서비스를 제공하는 연구데이터 서비스 플랫폼에서 데이터를 수집하여 서비스하는 일련의 과정에서 수행해야하는 품질관리에 대해 계획, 구축 및 운영, 활용단계로 나누어 논의하였다. 본 연구는 연구데이터 품질관리 수행 방안을 위한 지식 기반을 제공하는데 의의를 갖는다.

• 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.

• 한국정보시스템학회지:정보시스템연구
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• 제27권2호
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• pp.175-196
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• 2018

Purpose The purpose of this study is to identify problems of current educational facility data management and recommend a standardized terminology classification system as a solution. In addition, the research aims to present a preemptive and integrated disaster and safety management framework for educational facilities by seeking efficient business processes through secured data quality, systematic data management, and external data linkage and analysis. Design/methodology/approach A terminology classification system has been established through various processes including filtering and analysis of related data including laws, manuals, educational facilities accidents, and historical records. Furthermore, the terminology classification system has been further reviewed through several consultations with experts and practitioners. In addition, the accumulated data was refined according to the established standard terminology and an Excel database was developed. Based on the data, accident patterns occurred in educational facilities over the past 10 years were analyzed. Findings In the study, a template was developed to collect consistent data for the standardized disaster and safety management terminology classification system in educational facilities. In addition, the standardized data utilization methods are presented from the viewpoint of 'education facility disaster safety data management', 'data analysis and insight', 'business management through data', and 'leaping into big data management'.

• 디지털산업정보학회논문지
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• 제6권2호
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• pp.19-27
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• 2010

Historical archived traffic data management system enables a long term time-series analysis and provides data necessary to acquire the constantly changing traffic conditions and to evaluate and analyze various traffic related strategies and policies. Such features are provided by maintaining highly reliable traffic data through scientific and systematic management. Now, the management systems for massive traffic data have a several problems such as, the storing and management methods of a large volume of archive data. In this paper, we describe how to storing and management for the massive traffic data and, we propose methodology for logical and physical architecture, collecting and storing, database design and implementation, process design of massive traffic data.

• 한국경영과학회지
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• 제41권3호
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• pp.45-58
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• 2016

Data are important in an organization because they are used in making decisions and obtaining insights. Furthermore, given the increasing importance of data in modern society, data governance should be requested to increase an organization's competitive power. However, data governance concepts have caused confusion because of the myriad of guidelines proposed by related institutions and researchers. In this study, we re-established the concept of ambiguous data governance and derived the top-level components by analyzing previous research. This study identified the components of data governance and quantitatively analyzed the relation between these components by using DEMATEL and context analysis techniques that are often used to solve complex problems. Three higher components (data compliance management, data quality management, and data organization management) and 13 lower components are derived as data governance components. Furthermore, importance analysis shows that data quality management, data compliance management, and data organization management are the top components of data governance in order of priority. This study can be used as a basis for presenting standards or establishing concepts of data governance.

• 통합자연과학논문집
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• 제17권3호
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• pp.105-112
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• 2024

In the present age of artificial intelligence and metaverse, research on the importance of data and the amount of data is actively being conducted. Among these data, medical data contains the most sensitive information of individuals, so research on data generation, storage, management, and disposal is urgently needed. This study analyzed the status of medical data management in the United States, Europe, and Korea, and identified and analyzed medical data management laws and implementation status through working-level staff working in medical sites. As a result of the analysis, about 70% of medical professionals were able to identify the absence of recognition and management of medical data. The survey subjects were limited to Gwangju and Jeollanam-do, and 237 medical workers were conducted. More than 54% of the awareness of medical record generation, storage, and management came out, but about 70% of the occupations except doctors, oriental doctors, and dentists did not recognize the medical record management method. As necessary for medical record management, cost and the need for professional managers were 91.4%. Through this study, it was confirmed that the expansion of legal education for medical workers, the enactment of related laws, and the need for sincere fostering of medical record managers were required.

• 한국CDE학회논문집
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• 제13권4호
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• pp.314-322
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• 2008

This paper introduces an integration of Product Data Management (PDM) and Software Configuration Management (SCM). PDM and SCM have supported development of mechanical products and software products respectively. The importance of software components in the current products increases rapidly since the software enables the products to satisfy various customer requirements efficiently. Therefore the current product development needs enhanced product data management that can control both the hardware and software data seamlessly. This paper proposes an extended product data model for integrating SCM into PDM. The extension enables PDM document management to support the version control for software development. It also enables engineers to control both the software and hardware parts as integrated data objects during product configuration and engineering change management. The proposed model is implemented by using a commercial Product Lifecycle Management (PLM) system and a development of a network based robot system is tested by the implemented product development environment.

• 한국문헌정보학회지
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• 제57권3호
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• pp.279-301
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• 2023

데이터 중심의 연구환경으로 빠르게 변화하고 있다. 이에 따라 국내 대학도서관에서도 대학의 연구자를 지원하기 위한 연구데이터 관리 서비스 구축 및 운영을 준비하고 있다. 본 연구는 과학기술특성화 대학도서관에서 연구자를 지원하기 위한 연구데이터 관리 서비스를 제안하고자 설계되었다. 해당 서비스를 제안하기 위하여 해외 및 국내 과학기술특성화 대학 중 11곳을 선택하여 해당 기관의 연구데이터 관리 서비스를 분석하였다. 분석 결과, 연구데이터 관리, 전자 연구노트 그리고 RDM 교육으로 핵심 카테고리를 도출하였으며 특히, '연구데이터 관리' 카테고리는 DMP, 데이터 수집, 데이터 관리, 데이터 보존, 데이터 공유 및 출판, 데이터 재사용, 인프라 및 도구 그리고 RDM 가이드 및 정책으로 구성하였다. 본 연구 결과는 과학기술특성화 대학도서관에서 연구데이터 관리 서비스를 도입하고 운영하는데 도움이 될 것이다.