• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.109-116
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• 2008

The rapid change of computer and Internet environments produces a lot of data of various quality, Because this fact affects enterprise and organization, it demands the level evaluation on data quality, Thus, we propose mathematical model for quality evaluation on the base of data quality in this paper. And we propose the analysis model(web evaluation model, DQnA)) that analyzes and maintains data quality.

• Journal of the Korean Society for information Management
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• v.40 no.3
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• pp.77-98
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• 2023

Large Language Models (LLMs) are becoming the major trend in the natural language processing field. These models were built based on research data, but information such as types, limitations, and risks of using research data are unknown. This research would present how to analyze and evaluate the LLMs that were built with research data: LLaMA or LLaMA base models such as Alpaca of Stanford, Vicuna of the large model systems organization, and ChatGPT from OpenAI from the perspective of research data. This quality evaluation focuses on the validity, functionality, and reliability of Data Quality Management (DQM). Furthermore, we adopted the Holistic Evaluation of Language Models (HELM) to understand its evaluation criteria and then discussed its limitations. This study presents quality evaluation criteria for LLMs using research data and future development directions.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.113-114
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• 2012

게임이 기획, 그래픽, 프로그램의 복합적 기능을 가진 콘텐츠이지만 특별히 수준 높은 그래픽 데이터는 이용자의 만족도, 몰입 등에 긍정적인 영향을 미치는 중요한 영역이다. 그러나 아직 국내외에 게임 그래픽 데이터의 품질을 객관적으로 평가할 수 있는 기준이 마련되어 있지 않다. 이에 따라 본 논문에서는 게임 그래픽 데이터의 품질 평가를 위한 지표를 개발하는 프로세스를 제안한다. 제안된 프로세스는 그래픽 데이터의 품질 평가 영역 추출을 위한 방법과 세부 평가 지표 마련을 위한 평가 항목 추출방법들로 구성된다. 본 연구 결과는 고품질 게임 개발을 위한 품질평가 지표 개발에 적용될 것이며 관련 분야의 품질평가 모델 개발의 참조 모델이 될 것이다. 향후 본 연구는 국내외 게임 그래픽 데이터의 품질평가 표준안 설계 개발로 진행될 예정이다.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.83-105
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• 2013

There are two ways to assess data quality : measurement of data itself and assessment of data quality management process. Recently maturity assessment of data quality management process is used to ensure and certify the data quality level of an organization. Following this trend, the paper presents the process reference model which is needed to assess data quality management process maturity. First, the overview of assessment model for data quality management process maturity is presented. Second, the process reference model that can be used to assess process maturity is proposed. The structure of process reference model and its detail processes are developed based on the process derivation approach, basic principles of data quality management and the basic concept of process reference model in SPICE. Furthermore, characteristics of the proposed model are described compared with ISO 8000-150 processes.

• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.119-128
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• 2007

In this paper, We study on the Data Quality Model of ISO/IEC 25012 among the Software product Quality Requirements and Evaluation(SQuaRE) in ISO/IEC 25000 Series. Because of the increasing data, user require the accuracy data, recent data, suitable data for used tools, complied security and not open to be public. We research the data quality management in the point of application of be affect influenced low quality in business. We propose the testing items and we propose the method of the evaluation proposed testing items. We study on the basis international Standards ISO/IEC 25012 and ISO/IEC 9126-2 and we proposed the testing method quantitatively on the basis of ISO/IEC 25000.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.21-25
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• 2014

Now, almost enterprise is applying data analysis method using the information systems on based information technology. The data analysis is focusing on the Quality of the data affecting the decision-making of various companies. This is the result of the data quality is due to the important role in the various parts as well as the effective operation of the enterprise. In this study, we describe about the data quality assessment models that are currently being studied. Based on this, we describe about the adaptive DQnA model being utilized for data quality analysis, and discuss about the quality analysis using this method.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.533-545
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• 2014

According to the evolving of IT technologies, the amount of data we are facing increasing exponentially. Thus, the technique for managing and analyzing these vast data that has emerged is a distributed processing system of big data. A quality evaluation for the existing distributed processing systems has been proceeded by the structured data environment. Thus, if we apply this to the evaluation of distributed processing systems of big data which has to focus on the analysis of the unstructured data, a precise quality assessment cannot be made. Therefore, a study of the quality evaluation model for the distributed processing systems is needed, which considers the environment of the analysis of big data. In this paper, we propose a new quality evaluation model by deriving the quality evaluation elements based on the ISO/IEC9126 which is the international standard on software quality, and defining metrics for validating the elements.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.9
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• pp.689-699
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• 2013

During extreme ionospheric storms, anomalous ionospheric delays and gradients could cause potential integrity threats to users of GNSS (Global Navigation Satellite System) augmentation systems. GNSS augmentation ground facilities must monitor these ionospheric anomalies defined by a threat model and alarm the users of safely-of-life applications within time-to-alerts. Because the ionospheric anomaly threat model is developed using data collected from GNSS reference stations, the use of poor-quality data can degrade the performance of the threat model. As the total number of stations increases, the number of station with poor GNSS data quality also increases. This paper analyzes the quality of data collected from Korean GPS reference stations using comprehensive GNSS data quality check algorithms. The results show that the range of good and poor qualities varies noticeably for each quality parameter. Especially erroneous ionospheric delay and gradients estimates are produced due to poor quality data. The results obtained in this study should be a basis for determining GPS data quality criteria in the development of ionospheric threat models.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.59-65
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• 2019

In this paper, we propose a software quality measurement metrics of ISO / IEC 25023, which is newly proposed for software quality evaluation, to compare the difference with ISO / IEC 9126-2 which was used for software quality evaluation. In this paper, we propose a method for evaluating the quality of reliability based on the software reliability growth model among the eight quality characteristics presented in ISO / IEC 25023. Based on ISO / IEC 25023, software-quality evaluations demonstrate that there is some risk in evaluating reliability when based on data.

• Informatization Policy
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• v.22 no.1
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• pp.28-46
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• 2015

Although the demand for the use of public data increases in accordance with the expansion of Government 3.0, the poor level of data quality and its management currently implemented is becoming obstacles to opening data to the public. To improve the efficiency of management, linkage and usage for data, standardized processes for data quality management have to be prepared and appropriate data quality assessment criteria should be established. In this paper, we propose the organizational maturity model that can assess the public data quality management level. This model consists of the process reference model and the measurement framework. Fifteen processes grouped by the PDCA cycle are defined in the process reference model. The measurement framework measures the organizational maturity level based on process capability levels. The organizational maturity model can be used to establish objectives and directions for public data quality improvement by diagnosis of current level of public data quality management and problem solving. This model can also facilitate open to the private sector and activate usage of stable public data through reliability enhancement.