• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.217-222
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• 2020

This study is about the data quality management evaluation model. As the information and communication technology is advanced and the importance of storage and management begins to increase, the guam feeling for data is increasing. In particular, interest in the fourth industrial revolution and artificial intelligence has been increasing recently. Data is important in the fourth industrial revolution and the era of artificial intelligence. In the 21st century, data will likely play a role as a new crude oil. It can be said that the management of the quality of this data is very important. However, research is being conducted at a practical level, but research at an academic level is insufficient. Therefore, this study examined factors affecting data quality management for experts and suggested implications. As a result of the analysis, there was a difference in the importance of data quality management.

• Information Systems Review
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• v.6 no.2
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• pp.65-77
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• 2004

Business intelligence assumes an integrated and inter-connected information resources. To manage an integrated database, we need to trace data transformation processes from its outset. For this purpose, this study proposes an extended Information Structure Graph that models data transformation steps in addition to data transformation structures. Using the graph, we can identify relationship among data entities and assign data quality measures to each nodes or arcs of a graph, thus eases management of data and enhancing their quality.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.63-69
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• 1999

The purpose of this study is to develop Water Quality Management System(WQMS), which performs calculating pollutant discharge and forecasting water quality with water pollution model. Operational water quality management requires not only controlling pollutants but acquiring and managing exact information. A GIS software, ArcView was used to enter or edit geographic data and attribute data, and MapObject was used to customize the user interface. PCI, a remote sensing software, was used for deriving land cover classification from 20 m resolution SPOT data by image processing. WQMS has two subsystems, Database Subsystem and Modelling subsystem. Database subsystem consisted of watershed data from digital map, remote sensing data, government reports, census data and so on. Modelling subsystem consisted of NSPLM(NonStorm Pollutant Load Model)-SPLM(Storm Pollutant Load Model). It calculates the amount of pollutant and predicts water quality. This two subsystem was connected through graphic display module. This system has been calibrated and verified by applying to Mokhyun stream watershed.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.763-774
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• 2006

The purpose of this study is to identify relationship among service quality, service value and customer satisfaction of food service industries. The respondents included 120 customers of K-restaurant in the survey. Data were analyzed by confirmatory factor analysis and cause-effect analysis among the constructs. After research model testing, the following results was obtained : Service Value was influenced directly and positively by the service quality. And customer satisfaction was influenced directly and positively by the service value. But customer satisfaction was not influenced directly and positively by the service quality.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7985-7987
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• 2014

Introduction: Khon Kaen Cancer Registry (KKCR) was established in 1984. KKCR aims to collect all cancer cases in Khon Kaen Province. The poorly qualified data may lead to distort the cancer burden and misinterpretation of policy maker. Objective: To assess data quality in childhood cancer between 1990 and 2007 in Khon Kaen Province, Thailand. Materials and Methods: Data of childhood cancer cases aged less than 20 years diagnosed during 1990-2007 were retrieved from the population-based data set of KKCR. All childhood cancer data were verified before data entry. Internal consistency, percentage of morphological verification (MV%) and cancer cased of the basis of diagnosis by death certificate only (DCO%) were evaluated. The age-adjusted rate (ASR) was calculated by standard method. Results: The data of childhood cancer from KKCR is acceptably qualified which reflects the quality of the whole registration.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.325-335
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• 2006

This paper evaluates and maps the quality of life in the Atlanta, Georgia metropolitan area in 2000. Three environmental variables from Landsat TM data, four socioeconomic variables from census data, and a hazard-related variable from toxic release inventory (TRI) database were integrated into a geographic information system (GIS) environment for the quality of life assessment. To solve the incompatibility problem in areal units among different data, the four socioeconomic variables aggregated by zonal units were spatially disaggregated into individual pixels. Principal components analysis (PCA) was employed to integrate and transform environmental, socioeconomic, and hazard-related variables into a resultant quality of life score for each pixel. Results indicate that the highest quality of life score was found around Sandy Springs, Roswell, Alphretta, and the northern parts of Fulton County along Georgia 400 whereas the lowest quality of life score was clustered around Smyma of Cobb County, the inner city of Atlanta, and Hartsfield-Jackson International Airport. The results also reveals that normalized difference vegetation index (NDVI) and relative risk from TRI facilities are two versatile indicators of environmental and socioeconomic quality of an urban area in the United States.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.885-890
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• 2007

This study is to investigate factors that affect usage change in mobile data service (MDS). In the first, an exploratory study based on 378 survey responses was conducted to learn about important decision factors of MDS usage. It revealed discrepancy between the influencing forces of usage increase and those of usage decrease. Based on the findings from the exploratory study and the two-factor theory, we postulated information quality as the motivator and system quality as the de-motivator (or hygiene) of MDS. Then, a confirmative study was undertaken on their respective role in encouraging and discouraging the usage of mobile data service. A research model was proposed and subsequent hypotheses were empirically tested with partial least square (PLS) based on 478 responses from the users of mobile data service. It was learned that information quality (as a motivator) was positively associated with usage increase in mobile data service, but system quality (as a de-motivator) was not. Also, system quality was negatively associated with usage decrease, but information quality was not. Lastly, their association strength was partially moderated by the type of motivation for using MDS.

• Agribusiness and Information Management
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• v.3 no.1
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• pp.11-22
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• 2011

This study describes the development of a web-based system that collects all data generated in the research conducted to set pre-harvest residue limits (PHRLs) for agricultural product safety control. These data, including concentrations of pesticide residues, limit of detection, limit of quantitation, recoveries, weather charts, and growth rates, are incorporated into a database, a regression analysis of the data is performed using statistical techniques, and the PHRL for an agricultural product is automatically computed. The development and establishment of this system increased the efficiency and improved the reliability of the research in this area by standardizing the data and maintaining its accuracy without temporal or spatial limitations. The system permits automatic computation of the PHRL and a quick review of the goodness of fit of the regression model. By building and analyzing a database, it also allows data accumulated over the last 10 years to be utilized.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.509-522
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• 2018

Purpose: The purpose of this study was to propose more accurate mathematical model which can represent result of government quality assurance activity, especially corrective action and flaw. Methods: The collected data during government quality assurance activity was represented through histogram. To find out which distributions (Poisson distribution, Zero-Inflated Poisson distribution) could represent the histogram better, this study applied Pearson's correlation coefficient. Results: The result of this study is as follows; Histogram of corrective action during past 3 years and Zero-Inflated Poisson distribution had strong relationship that their correlation coefficients was over 0.94. Flaw data could not re-parameterize to Zero-Inflated Poisson distribution because its frequency of flaw occurrence was too small. However, histogram of flaw data during past 3 years and Poisson distribution showed strong relationship that their correlation coefficients was 0.99. Conclusion: Zero-Inflated Poisson distribution represented better than Poisson distribution to demonstrate corrective action histogram. However, in the case of flaw data histogram, Poisson distribution was more accurate than Zero-Inflated Poisson distribution.