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

• Proceedings of the Korean Society for Quality Management Conference
• /
• 2006.04a
• /
• pp.142-148
• /
• 2006

Data mining is a useful tool for analyzing data from different perspectives and for summarizing them into useful information. Recently, the data mining methods are applied to solving quality problems of the manufacturing processes. This paper discusses the problems of construction of a quality mining system, which is based on the various data mining methods. The quality mining system includes recipe optimization, significant difference test, finding critical processes, forecasting the yield. The contents and system of this paper are focused on the TFT-LCD manufacturing process. We also provide some illustrative field examples of the quality mining system.

• Journal of Korean Society for Quality Management
• /
• v.11 no.2
• /
• pp.25-31
• /
• 1983

Often arrangement for effective product assessment and audit have not been completely satisfactory. The underlying reasons are: (a) The lack of early evidence of new unit quality. (b) The collection and processing of data. (c) Ineffective data analysis techniques. (d) The variability of information on which decision making is based. Because of the nature of the product the essential outputs from an affective QA organization would be: (a) Confirmation of new unit quality. (b) Detection of failures which are either epidemic or slowly degradatory. (c) Identification of failure cases. (d) Provision of management information at the right time to effect the necessary corrective action. The heart of an effective QA scheme is the acquisition and processing of data. With the advent of data processing for quality monitoring becomes feasible in an automotive testing environment. This paper shows how the method enables us to use Automotive Testing data for the cost benefits of QA management.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.363-370
• /
• 1999

Remotely sensed data is regarded as a potentially effective data source for the measurement of water quality and for the environmental change of water bodies. In this study, we measured the spectral reflectance by using multi-spectral image of low resolution camera(LRC) which will be loaded in the OSMI multi-purpose satellite(KOMPSAT) scheduled to be launched on 1999 to use the data in analyzing water pollution. We also investigated the possibility of extraction of water quality factors in water bodies by using remotely sensed low resolution data such as NOAA/AVHRR. In this study, Shiwha-District and Sang-Sam Lake was set up as the subject areas for the study. In this part of the study, we measured the spectral reflectance of the water surface to analyze the radiance of the water bodies in low resolution spectral band and tried to analyze the water quality factors in water bodies by using radiance feature from another remotely sensed data such as NOAA/AVHRR. As the method of this study, first, we measured the spectral reflectance of the water surface by using SFOV( Single Field of View) to measure the reflectance of water quality analysis from every channel in LRC spectral band(0.4~O.9${\mu}{\textrm}{m}$). Second, we investigated the usefulness of ground truth data and the LRC data by measuring every spectral reflectance of water quality factors. Third, we analyzed water quality factors by using the radiance feature from another remotely sensed data such as NOAA/AVHRR. We carried out ratio process of what we selected Chlorophyll-a and suspended sediments as the first factors of the water quality. The results of the analysis are below. First, the amount of pollutants of Shiwha-Lake has been increasing every you since 1987 by factors of eutrophication. Second, as a result of the reflectance, Chlorophyll-a represented high spectral reflectance mainly around 0.52${\mu}{\textrm}{m}$ of green spectral band, and turbidity represented high spectral reflectance at 0.57${\mu}{\textrm}{m}$. But suspended sediments absorbed high at 0.8${\mu}{\textrm}{m}$. Third, Chlorophyll-a and suspended sediments could have a distribution chart as a result of the water quality analysis by using NOAA/AVHRR data.

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

• Atmosphere
• /
• v.22 no.3
• /
• pp.299-307
• /
• 2012

This study is aimed to validate errors for detected suspicious temperature data using various quality control procedures for 61 weather stations in the Republic of Korea. The quality control algorithms for temperature data consist of four main procedures (high-low extreme check, internal consistency check, temporal outlier check, and spatial outlier check). Errors of detected suspicious temperature data are judged by examining temperature data of nearby stations, surface weather charts, hourly temperature data, daily precipitation, and daily maximum wind direction. The number of detected errors in internal consistency check and spatial outlier check showed 4 days (3 stations) and 7 days (5 stations), respectively. Effective and objective methods for validation errors through this study will help to reduce manpower and time for conduct of quality management for temperature data.

• Journal of Korean Society for Geospatial Information Science
• /
• v.4 no.2 s.8
• /
• pp.123-136
• /
• 1996

Lately, the demand for data standardization become increased to obtain various data jointly along with development of information technology and diversity of society. Thus the research on tile definition and evaluation of data quality indicating accuracy and confidence of geospatial data, is required for this standardization. In this study, by virtue of comparison of definitions and evaluation methods of data quality element being selected from representative countries, the following results were obtained: (1) Application of ISO/TC211's Draft having accepted evaluation standard to KSDTS(Korea Spatial Data Transfer Standard) is desirable for definitions of data quality elements. (2) This study presented the quality evaluation of much more resonable geospatial data accompaning with quality element. Furthermore, this study suggests that this evaluation be applicable to KSDTS and be contained in the digital map product specification of National Geography Institute with more clearness of a report form of data quality evaluation result. (3) Studies on various sampling methods, establishment of AQL(Acceptable Quality Level) suitable for our country, and computer programming which can rapidly and automatically evaluate mass much of data are required.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.11-18
• /
• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• Journal of Information Technology Applications and Management
• /
• v.10 no.4
• /
• pp.103-118
• /
• 2003

This study presents a database quality evaluation framework. As a way to build a framework, this study expands data quality management to include data transformation processes as well as data. Further, an information structure graph is applied to represent data transformations processes. An information structure graph is absed on a relational database scheme. Thus, data transformation processes may be stored in a relational database. This kind of integration of data transformation metadata with technical metadata eases evaluation of database qualities and their causes.

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.4
• /
• pp.823-833
• /
• 2005

This paper deals the TFT-LCD process and quality, process control problems of the process. For improvement of the process quality and yield, we apply a data mining technique to the LCD industry. And some unique quality features of the LCD process are also described. We describe some preceding researches first and relate to the TFT-LCD process and the problems of data mining in the process. Also we tried to observe the problems which need to solve first and the features from description below hazard must be considered a quality mining in LCD industry.