• Journal of Computing Science and Engineering
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• 제2권3호
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• pp.221-248
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• 2008

Data provenance is the background knowledge that enables a piece of data to be interpreted and used correctly within context. The importance of tracking provenance is widely recognized, as witnessed by significant research in various areas including e-science, homeland security, and data warehousing and business intelligence. In order to further advance the research on data provenance, however, one must first understand the research that has been conducted to date and identify specific topics that merit further investigation. In this work, we develop a framework based on semiotics theory to assist in analyzing and comparing existing provenance research at the conceptual level. We provide a detailed review of data provenance research and compare and contrast the research based on d semiotics framework. We conclude with an identification of challenges that will drive future research in this field.

• 서비스연구
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• 제5권2호
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• pp.119-132
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• 2015

낮은 품질의 데이터가 기업의 효율적인 운영과 신속한 의사결정을 저해한다는 이슈가 제기되고 있다. 이에, 본 연구는 경영층의 지원과 경쟁력이 심화되고 있는 경영환경이 데이터 활용 품질관리 활동의 영향요인이 될 수 있는지, 해당 활동이 업무처리 또는 의사결정을 위한 데이터 활용을 촉진시킴으로써 고객 지향성에 긍정적인 영향을 미치는지 살펴보았다. 연구결과 데이터 활용 품질관리는 데이터를 업무처리에 직접적 또는 의사결정에 활용하는데 긍정적인 영향요인이 될 수 있으며, 데이터 활용이 고객지향성에 간접적인 효과를 줄 수 있음을 확인하였다. 본 연구는 직접적인 매출 향상의 성과를 기대할 수 있을 것 같지 않다는 인식으로 데이터 품질관리의 중요성을 간과하는 기업의 경영자들에게 데이터 품질관리활동의 가치 및 경영층의 지원에 대한 실무적 함의를 제시하였다.

• International Journal of Knowledge Content Development & Technology
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• 제13권1호
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• pp.75-100
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• 2023

With research data generation on the rise, Institutional Repositories (IR) are one of the tools to manage it. However, the variety of data practices across institutions, domains, communities, etc., often requires dedicated studies in order to identify the research data management (RDM) require- ments and mapping them to IR features to support them. In this study, we investigated the data practices for a few national universities in North Macedonia, including 110 participants from different departments. The methodology we adopted to this end enabled us to derive some of the key RDM requirements for a variety of data-related activities. Finally, we mapped these requirements to 6 features that our participants asked for in an IR solution: (1) create (meta)data and documentation, (2) distribute, share, and promote data, (3) provide access control, (4) store, (5) backup, and (6) archive. This list of IR features could prove useful for any university that has not yet established an IR solution.

• 한국경영과학회지
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• 제42권1호
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• pp.53-63
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• 2017

Managing the quantitative measurement of the data control activities in enterprise wide is important to secure management of data governance. However, research on data governance is limited to concept definitions and components, and data governance research on evaluation models is lacking. In this study, we developed a model of quantitative assessment for data governance including the assessment area, evaluation index and evaluation matrix. We also, proposed a method of developing the model of quantitative assessment for data governance. For this purpose, we used previous studies and expert opinion analysis such as the Delphi technique, KJ method in this paper. This study contributes to literature by developing a quantitative evaluation model for data governance at the early stage of the study. This paper can be used for the base line data in objective evidence of performance in the companies and agencies of operating data governance.

• 설비공학논문집
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• 제25권4호
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• pp.216-224
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• 2013

Facility data is created throughout the design and construction phase. But the most facility managers bear significant costs that arise from the lack of interoperability with facility lifecycle. This paper is concerned with the way to collect facility data using BIM technology. The aim of this paper is to suggest BIM data modeling guide for the facility management using the information that need to be delivered from design and construction phase to operation and management phase. The BIM data modeling guide focus on the properties of mechanical equipment. It is to be hoped that this study will contribute to collect facility data from as-built BIM data and to build facility management system database without difficulty.

• 한국건설관리학회논문집
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• 제16권2호
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• pp.77-85
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• 2015

현재 건설공사 사후평가시스템 입력과정에서 자료가 오류 및 누락이 발생되는 경우가 많아 데이터의 신뢰성이 떨어지는 경우가 많다. 이에 따라 본 연구에서는 사후평가 결과분석의 신뢰성 확보를 위해 건설공사 사후평가시스템의 입력누락 및 입력오류에 대한 세부적인 분석을 실시하였다. 분석결과, 건설사업 초기 단계일수록 자료의 미입력된 자료의 비중이 높게 나타났다. 그리고 분석된 등록오류로는 참고자료의 부정확, "원" 단위 오류, 입력항목에 대한 이해부족 등으로 나타났다.

• 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'.

• 대한안전경영과학회지
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• 제17권3호
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• pp.89-96
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• 2015

The study was conducted with statical analysis of data (828 data in 2010, 752 data in 2012, 648 data in 2014) in order to evaluate laboratory awareness difference of research employees working in different types of universities. Results of the study were as follows: First, university institutes in the order of polytechnic colleges, university, and junior college showed the highest laboratory safety awareness in 'awareness and education of laboratorial safety regulation' and 'awareness in laboratory risk factors'. Second, the difference in safety awareness of universities by year(years that conducted current status survey) was the highest in year 2014, then in 2010, and in 2008. Third, the difference of research employees working for laboratory safety management by year(years that conducted current status) showed that university had the highest laboratory safety awareness in year 2010, but it changed to polytechnic colleges in year 2012 and 2014. Through this study, we could recognize the difference in safety awareness of research employees working in university institutes.

• 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.

• Journal of Construction Engineering and Project Management
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• 제1권1호
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• pp.37-43
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• 2011

Labor productivity is a significant factor associated with controlling time, cost, and quality. Many researchers have developed models to define methods of measuring the relationship between productivity and various parameters such as the size of working area, maximum working hours, and the crew composition. Most of the previous research has focused on estimating productivity; however, this research concentrates on estimating labor productivity and developing time and cost data for repetitive concrete pouring activity. In Korea, "Standard Estimating" only entails the average productivity data of the construction industry, and it is difficult to predict the time and cost spent on any particular project. As a result, errors occur in estimating duration and cost for individual activities or projects. To address these issues, this research sought to collect data, measure productivity, and develop time and cost data using labor productivity based on field conditions from the collected data. A probabilistic approach is also proposed to develop data. A case study is performed to validate this process using actual data collected from construction sites. It is possible that the result will be used as the EVMS baseline of cost management and schedule management.