• Title, Summary, Keyword: Data quality diagnosis

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A Study of Big Data Domain Automatic Classification Using Machine Learning (머신러닝을 이용한 빅데이터 도메인 자동 판별에 관한 연구)

  • Kong, Seongwon;Hwang, Deokyoul
    • The Journal of Bigdata
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    • v.3 no.2
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    • pp.11-18
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    • 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.

A Study on Automation of Big Data Quality Diagnosis Using Machine Learning (머신러닝을 이용한 빅데이터 품질진단 자동화에 관한 연구)

  • Lee, Jin-Hyoung
    • The Journal of Bigdata
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    • v.2 no.2
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    • pp.75-86
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    • 2017
  • In this study, I propose a method to automate the method to diagnose the quality of big data. The reason for automating the quality diagnosis of Big Data is that as the Fourth Industrial Revolution becomes a issue, there is a growing demand for more volumes of data to be generated and utilized. Data is growing rapidly. However, if it takes a lot of time to diagnose the quality of the data, it can take a long time to utilize the data or the quality of the data may be lowered. If you make decisions or predictions from these low-quality data, then the results will also give you the wrong direction. To solve this problem, I have developed a model that can automate diagnosis for improving the quality of Big Data using machine learning which can quickly diagnose and improve the data. Machine learning is used to automate domain classification tasks to prevent errors that may occur during domain classification and reduce work time. Based on the results of the research, I can contribute to the improvement of data quality to utilize big data by continuing research on the importance of data conversion, learning methods for unlearned data, and development of classification models for each domain.

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Medical Diagnosis Inference using Neural Network and Discriminant Analyses

  • Chang, Duk-Joon;Kwon, Yong-Man
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.2
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    • pp.511-518
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    • 2008
  • Medical diagnosis systems have been developed to make the knowledge and expertise of human experts more widely available, therefore achieving high-quality diagnosis. In this study, in order to support the diagnosis by the medical diagnosis system, we have preformed medical diagnosis inference three times: first by a neural network with the backpropagation algorithm, secondly by a discriminant analysis with all of the variables, and thirdly by a discriminant analysis with the selected variables. A discussion on comparison of these three methods has been provided.

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Development of Power Quality Management System with Power Quality Diagnosis Functions

  • Chung Il-Yop;Won Dong-Jun;Ahn Seon-Ju;Kim Joong-Moon;Moon Seung-Il;Seo Jang-Cheol;Choe Jong-Woong;Jang Gil-Soo
    • Journal of Electrical Engineering and Technology
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    • v.1 no.1
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    • pp.28-34
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    • 2006
  • Recently, in accordance with the development of IT technology, it is prevalent for power quality monitors to be connected to each other via networks and share their data because such networks provide system-wide insights to customers concerning power quality. Those systems can alarm and display power quality events for the convenience of customers. However, if a power quality event occurs, it is difficult for customers to determine its cause and solution because the systems do not provide appropriate power quality diagnosis functions. The power quality management system presented in this paper has been developed to provide customers with various power quality diagnosis functions so that they can cope well with power quality problems with the right measure in the right place. This paper presents the structure and functions of the developed power quality management system and shows some results of the power diagnosis functions.

Quality Diagnosis and Improvement of Fisheries Census Statistic (어업조사통계의 품질진단과 개선에 관한 연구)

  • Pyo, Hee-Dong;Kim, Jong-Chun
    • Journal of Fisheries and Marine Sciences Education
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    • v.22 no.4
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    • pp.553-565
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    • 2010
  • The paper aims to evaluate the quality of fisheries census statistic and to provide some desirable directions and improvements for the future fisheries census, conducted by the Government. For the quality diagnosis of fisheries census statistic, specific processes of fisheries census and statistical qualities of each dimension are surveyed and evaluated by a Government's practician, two external examiners and a research group. Results show that census design, data analysis and quality control are evaluated relatively low in specific processes, and accessibility and comparability are evaluated relatively lower than relevance, accuracy, timeliness and consistency in statistical qualities. For minimizing the sampling errors, the probability proportion method should be employed in sampling methods from currently simple sampling method. In addition, fisheries census statistic is desirable to include and compare with those of different countries for consumer oriented data system.

A New Study on Vibration Data Acquisition and Intelligent Fault Diagnostic System for Aero-engine

  • Ding, Yongshan;Jiang, Dongxiang
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • pp.16-21
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    • 2008
  • Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

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Development of Construction Model of Disease Classification on Clinical Diagnosis in Ophthalmology (임상진단명에 따른 질병분류체계 구축모형 개발 - 안과를 대상으로 -)

  • Suh, Jin-Sook;Shin, Hee-Young;Kee, Chang-Won
    • Quality Improvement in Health Care
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    • v.10 no.2
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    • pp.204-215
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    • 2003
  • Background : ICD-10 Classification, which is used domestically as well as internationally, has limited use in the clinical practice since it is developed for at disease statistics and epidemiology. Therefore, the purposes of this study were to improve the quality of diagnosis by constructing a new disease classification based on the diagnoses doctors currently make in the clinical setting and connecting this classification with OCS and EMR, and to meet the demands of doctors for high quality medical study data in medical research. Methods : The specialists in each ophthalmic subfield collected clinical diagnoses and abbreviations based on the ophthalmology textbooks and confirmed the classifications. Total number of clinical diagnoses collected was totaled 672, for which ideal diagnoses had been selected and a new model of disease classification model in connection with ICD-10 was constructed. The constructed classification of clinical diagnoses consisted of six steps: the first step was the classification by ophthalmic subspecialty field; the second to fifth steps were the detailed classification by each specialty field; the sixth step was the classification by site. Results : After introducing the new disease classification, research on the use and a pre-post comparison was conducted. The result from the research on the use of the clinical diagnoses in inpatient and outpatient care has shown a gradually increasing tendency. From the pre-post comparison of EMR discharge summary diagnoses, the result demonstrated that the diagnosis was stated correctly and in detail. Since the diagnosis was stated correctly, code classification became correct as well, which makes it possible to construct high quality medical DB. Conclusion : This construction of clinical diagnoses provides the medical team with high quality medical information. It is also expected to increase the accuracy and efficiency of service in the department of medical record and department of insurance investigation. In the future, if hospitals wish to construct a classification of clinical diagnosis and a standard proposal of clinical diagnosis is presented by a medical society, the standardization of diagnosis seems to be possible.

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Analysis of Healthcare Quality Indicators using Data Mining and Development of a Decision Support System (데이터마이닝을 이용한 의료의 질 측정지표 분석 및 의사결정지원시스템 개발)

  • Kim, Hye Sook;Chae, Young-Moon;Tark, Kwan-Chul;Park, Hyun-Ju;Ho, Seung-Hee
    • Quality Improvement in Health Care
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    • v.8 no.2
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    • pp.186-207
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    • 2001
  • Background : This study presented an analysis of healthcare quality indicators using data mining and a development of decision support system for quality improvement. Method : Specifically, important factors influencing the key quality indicators were identified using a decision tree method for data mining based on 8,405 patients who discharged from a medical center during the period between December 1, 2000 and January 31, 2001. In addition, a decision support system was developed to analyze and monitor trends of these quality indicators using a Visual Basic 6.0. Guidelines and tutorial for quality improvement activities were also included in the system. Result : Among 12 selected quality indicators, decision tree analysis was performed for 3 indicators ; unscheduled readmission due to the same or related condition, unscheduled return to intensive care unit, and inpatient mortality which have a volume bigger than 100 cases during the period. The optimum range of target group in healthcare quality indicators were identified from the gain chart. Important influencing factors for these 3 indicators were: diagnosis, attribute of the disease, and age of the patient in unscheduled returns to ICU group ; and length of stay, diagnosis, and belonging department in inpatient mortality group. Conclusion : We developed a decision support system through analysis of healthcare quality indicators and data mining technique which can be effectively implemented for utilization review and quality management in a healthcare organization. In the future, further number of quality indicators should be developed to effectively support a hospital-wide Continuous Quality Improvement activity. Through these endevours, a decision support system can be developed and the newly developed decision support system should be well integrated with the hospital Order Communication System to support concurrent review, utilization review, quality and risk management.

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Data-based On-line Diagnosis Using Multivariate Statistical Techniques (다변량 통계기법을 활용한 데이터기반 실시간 진단)

  • Cho, Hyun-Woo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.1
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    • pp.538-543
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    • 2016
  • For a good product quality and plant safety, it is necessary to implement the on-line monitoring and diagnosis schemes of industrial processes. Combined with monitoring systems, reliable diagnosis schemes seek to find assignable causes of the process variables responsible for faults or special events in processes. This study deals with the real-time diagnosis of complicated industrial processes from the intelligent use of multivariate statistical techniques. The presented diagnosis scheme consists of a classification-based diagnosis using nonlinear representation and filtering of process data. A case study based on the simulation data was conducted, and the diagnosis results were obtained using different diagnosis schemes. In addition, the choice of future estimation methods was evaluated. The results showed that the performance of the presented scheme outperformed the other schemes.

Customer Loyalty to Health Services According to Hospital Type (병원 규모별 의료소비자의 고객충성도 형성요인)

  • Kim, Seon-Ju;Cho, Young-Jin
    • The Korean Journal of Health Service Management
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    • v.10 no.4
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    • pp.13-23
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    • 2016
  • Objectives : This research used an exploratory approach to identify factors affecting business strategies due to changes in the healthcare market and customer loyalty factors. Methods : The research model was formulated using antecedents divided into diagnosis quality, employee attitudes, and servicescape. Moreover, differences in the structured model were analyzed according to hospital size. The data were gathered through surveys on clients, who has received care at participating hospitals. From the 200 that were distributed, 150 questionnaires were analyzed, to facilitate analysis of the research model. Results : The effects of diagnosis quality, employee attitudes, and servicescape, on customer loyalty were mediated by trust. We also found the differences between small and large hospitals. Conclusions : Customer loyalty in small hospitals was affected by servicescape, whereas that in large hospitals was affected by diagnosis quality and employee attitudes. The research results could be used to develop strategies to improve customer loyalty.