• The Journal of Bigdata
• /
• v.2 no.2
• /
• pp.87-93
• /
• 2017

The research on data quality has been performed for a long time. However, the research focused on structured data. With the recent digital revolution or the fourth industrial revolution, quality control of big data is becoming more important. In this paper, we analyze and classify big data quality types through previous research. The types of big data quality can be classified into value, data structure, process, value chain, and maturity model. Based on these comparative studies, this paper proposes a new standard, service quality of big data.

• Journal of Information Technology Services
• /
• v.10 no.3
• /
• pp.113-126
• /
• 2011

The smelting and the continuous casting of steel are important processes that determine the quality of steel products. Especially most of quality defects occur during solidification of the steel continuous casting process. Although quality control techniques such as six sigma, SQC, and TQM can be applied to the continuous casting process for improving quality of steel products, these techniques don't provide real-time analysis to identify the causes of defect occurrence. To solve problems, we have developed a detection model using decision tree which identified abnormal transactions to have a coarse grain structure. And we have compared the proposed model with models using neural network and logistic regression. Experiments on steel data showed that the performance of the proposed model was higher than those of neural network model and logistic regression model. Thus, we expect that the suggested model will be helpful to control the quality of steel products in real-time in the continuous casting process.

• Journal of Korean Society on Water Environment
• /
• v.20 no.6
• /
• pp.685-691
• /
• 2004

Water quality forecasting with long term flow is important for management and operation of river environment. However, it is difficult to set up and operate a physical model for water quality forecasting due to large uncertainty in the data required for model setting. Therefore, relatively simpler stochastic approaches are adopted for this problem. In this study we try several multivariate time series models such as ARMAX models for the possible substitute for water quality forecasting. Those models are applied to the BOD and COD levels at Noryangin station, Han river, and also evaluated the effect of release from Paldang dam on them. Monthly BOD and COD data from 1985 to 1991 (7 years) are used for model building and another two year data for model testing. As a result of the study, the effect of improvement on water quality is much more effective combining with the water quality improvement of dam release than considering only increment of dam release in the downstream Han river.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.45 no.1
• /
• pp.102-114
• /
• 2003

The objectives of the paper are to evaluate cell based pollutant loadings for different storm events, to monitor the hydrology and water quality of the Baran HP#6 watershed, and to validate AGNPS with the field data. Simplification was made to AGNPS in estimating storm erosivity factors from a triangular rainfall distribution. GIS-AGNPS interface model consists of three subsystems; the input data processor based on a geographic information system. the models. and the post processor Land use patten at the tested watershed was classified from the Landsat TM data using the artificial neural network model that adopts an error back propagation algorithm. AGNPS model parameters were obtained from the GIS databases, and additional parameters calibrated with field data. It was then tested with ungauged conditions. The simulated runoff was reasonably in good agreement as compared with the observed data. And simulated water quality parameters appear to be reasonably comparable to the field data.

• Journal of Environmental Health Sciences
• /
• v.41 no.2
• /
• pp.90-101
• /
• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Journal of Environmental Science International
• /
• v.8 no.3
• /
• pp.355-361
• /
• 1999

The aim of this study is to develop the water quality simulation model (BAYQUAL) that deal with the physical, chemical and biological aspects of fate/behavior of pollutants in the bay. BAYQUAL is a two dimensional, time-variable finite element water quality model based on the flow simulation model in bay(BAYFLOW). The algorithm is composed of a hydrodynamic module which solves the equations of motion and continuity, a pollutnat dispersion module which solves the dispersion-advection equation. The applicability and feasibility of the model are discussed by applications of the model to the Kwangyang bay of south coastal waters of Korea. Based on the field data, the BAYQUAL model was calibrated and verified. The results were in good agreement with measured value within relative error of 14% for COD, T-N, T-P. Numerical simulations of velocity components and tide amplitude(M2) were agreed closely with the actual data.

• Journal of Korean Academy of Nursing
• /
• v.31 no.1
• /
• pp.107-125
• /
• 2001

This study was designed to construct a structural model for quality of life of chronic gastric disease. The hypothetical model was developed based on the literature review and Pender's health promotion model. Data were collected by questionnaires from 459 patients with chronic gastric disease in a General Hospital from July 1999 to August 2000 in Seoul. Data analysis was done with SAS 6.12 for descriptive statistics and PC-LISREL 8.13 Program for Covariance structural analysis. The results are as follows : 1. The fit of the hypothetical model to the data was moderate, thus it was modified by excluding 1 path and including free parameters and 2 path to it. The modified model with path showed a good fitness to the empirical data ($\chi$2=934.87, p<.0001, GFI=0.88, AGFI=0.83, NNFI=0.86, RMSR =0.02, RMSEA=0.07). 2. The perceived barrier, health promoting behavior, self-efficacy, and self-esteem were found to have significant direct effects on the quality of life. 3. The health concept, health perception, emotional state, and social support were found to have indirect effects on quality of life of chronic gastric disease. In conclusion, the derived model in this study is considered appropriate in explaining and predicting quality of life of chronic gastric disease. Therefore it can effectively be used as a reference model for further studies and suggested direction in nursing practice.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.10
• /
• pp.1007-1013
• /
• 2020

This study aims to understand the determinant of the employee's intention to use information system within the framework of the Technology Acceptance Model (TAM) and Information System Success Model (ISSM). This paper also aims to examine the influence of ISSM dimension on the perceived usefulness and the perceived ease of use. The research respondents were 248 employees of Universitas Negeri Semarang (UNNES) who are users of the Financial Information System (SIKEU). Data was obtained using a questionnaire that was distributed online via Google form. The data analysis method used is Structural Equation Model (SEM) analysis using the Warp-PLS software. The results showed that the dimensions of TAM (perceived ease of use and perceived usefulness) had a positive and significant influence on the employee's intention to use SIKEU. The ISSM dimension (system quality and information quality) also had a significant influence, although other ISSM dimensions (service quality) had not been proven to have a significant influence on the employee's intention to use SIKEU. Moreover, the results showed that the employee's intention to use is a determinant of SIKEU's actual usage. Perceived ease of use was significantly determined by system quality, information quality, and service quality. In addition, the perceived usefulness was significantly determined by system quality and information quality.

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

• Journal of Korean Society of Water and Wastewater
• /
• v.21 no.3
• /
• pp.305-314
• /
• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model