• Journal of applied mathematics & informatics
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• 제41권5호
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• pp.1129-1143
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• 2023

Social media platforms have become an integral part of our daily lives, and they generate vast amounts of data that can be analyzed for various purposes. However, the quality of the data obtained from social media is often questionable due to factors such as noise, bias, and incompleteness. Enhancing data quality is crucial to ensure the reliability and validity of the results obtained from such data. This paper proposes an enhanced decision-making framework based on Business Decision Management Systems (BDMS) that addresses these challenges by incorporating a data quality enhancement component. The framework includes a backtracking method to improve plan failures and risk-taking abilities and a steep optimized strategy to enhance training plan and resource management, all of which contribute to improving the quality of the data. We examine the efficacy of the proposed framework through research data, which provides evidence of its ability to increase the level of effectiveness and performance by enhancing data quality. Additionally, we demonstrate the reliability of the proposed framework through simulation analysis, which includes true positive analysis, performance analysis, error analysis, and accuracy analysis. This research contributes to the field of business intelligence by providing a framework that addresses critical data quality challenges faced by organizations in decision-making environments.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.698-701
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• 2004

In many literatures, data mining has been used as a utilization of data warehouse and data collection. The biggest utilizations of data mining are for marketing and researches. This is solely because of the data available for this field is usually in large amount. The usability of the data mining is expandable also to the production process. While the object of research of the data mining in marketing is the customers and products, data mining in the production field is object to the so called 4MlE, man, machine, materials, method (recipe) and environment. All of the elements are important to the production process which determines the quality of the product. Because the final aim of the data mining in production field is the quality of the production, this data mining is commonly recognized as quality data mining. As the variables researched in quality data mining can be hundreds or more, it could take a long time to reveal the information from the data warehouse. Feature selection methodology is proposed to help the research take the best performance in a relatively short time. The usage of available simple statistical tools in this method can help the speed of the mining.

• 한국물환경학회지
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• 제32권6호
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• pp.511-519
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• 2016

National Institute of Environmental Research (NIER) has developed the Korean Reach File (KRF) for scientific and systematic analysis of variables related to water quality, pollutant sources and aquatic ecosystems in consideration of steam reach networks. The KRF provides a new framework for data production, storage, management and analysis for water related variables in relation to spatial characteristics, connections, and topologies of stream reaches. However, the current version of KRF (ver.2) has limited applicability because its nodes include not only the stream points based on topological characteristics but also those based on water quality monitoring stations, which may undermine its generality. In this study, a new version of KRF (ver.3) was designed and established to overcome the weak point of version 2. The version 3 is a generalization of the old KRF graphic data and it integrates the attribute data while separating it from the graphic data to minimize additional work that is needed for data association and search. We tested the KRF (ver.3) on actual cases and convenience and adaptability for each application was verified. Further research should focus on developing a database link model and real-world applications that are targeted to process event data.

• 한국환경과학회지
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• 제25권3호
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• pp.361-371
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• 2016

Ministry of Environment has been operating water quality monitoring network in order to obtain the basic data for the water environment policies and comprehensively understand the water quality status of public water bodies such as rivers and lakes. The observed water quality data is very important to analyze by applying statistical methods because there are seasonal fluctuations. Typically, monthly water quality data has to analyze that the transition comprise a periodicity since the change has the periodicity according to the change of seasons. In this study, trends, SOM and RDA analysis were performed at the Mulgeum station using water quality data for temperature, BOD, COD, pH, SS, T-N, T-P, Chl-a and Colon-bacterium observed from 1989 to 2013 in the Nakdong River. As a result of trends, SOM and RDA, the Mulgeum station was found that the water quality is improved, but caution is required in order to ensure safe water supply because concentrations in water quality were higher in the early spring(1~3 month) the most.

• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.971-977
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• 2021

The purpose of this research paper is: (1) to describe the service quality index; (2) describe the data quality index; and (3) describe the anti-corruption index of BPS Trenggalek, Indonesia. The approach chosen is quantitative with the type of survey research. The primary data collection technique was mainly based on a questionnaire distributed to 40 respondents, namely BPS service users in 5 (five) categories: the private sector, the banking industry, academics, offices, or agencies in Trenggalek Regency and universities. The results showed that the quality of BPS services was good and the data quality index where the respondents were satisfied with the data presented by BPS. Meanwhile, testing the anti-corruption index shows that BPS Trenggalek is very anti-corruption in providing services to consumers. The findings of this study suggested that to improve service quality, it is necessary to pay attention to several aspects, including published service requirements, easy requirements to be fulfilled, published procedure information, clear service process flow, published service times, and costs/tariffs are communicated. This study suggests updating data, data relevance, data accessibility, and data completeness to improve data quality. Furthermore, to maintain the very anti-corruption predicate, this study suggests maintaining service by upholding the prevailing ethics and norms.

• Asian Journal for Public Opinion Research
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• 제7권1호
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• pp.57-74
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• 2019

Researchers usually examine the quality of survey data by several conventional measures of reliability and validity. However, those measures are mainly designed to examine the quality of an individual measurement, rather than the quality of a data set as a whole. There is a relative lack of methods for evaluation of the overall data quality. This paper attempts to fill this gap. We propose using interviewers' assessments as one of criteria for evaluating the overall data quality. Interviewers are the ones who literally conduct and thus directly observe interviews. Taiwan's Election and Democratization Studies (TEDS) have required interviewers to assess how trustworthy the responses of each of their interviewees are, and to provide several descriptions about the process and environment of the interviews. We use this information to evaluate the data quality of TEDS surveys and compare it with the results from the conventional test-retest method. The findings are that the interviewer assessment is a fair indicator of the overall reliability of attitudinal questions but not a good indicator when factual questions are examined. Regarding the evaluation of data validity, more data is required to see whether or not interviewers' assessment is informative in terms of data quality.

• 산업공학
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• 제24권2호
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• pp.128-138
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• 2011

This research proposes a resource allocation model of Data QC (Quality Control) activities using COQ (Cost of Quality). The model has been developed based on a series of research efforts such as COQ classifications, weight determination of Data QC activities, and an aggregation approach between COQ and Data QC activities. In the first stage of this research, COQ was divided into the four typical classifications (prevention costs, appraisal costs, internal failure costs and external failure costs) through the opinions from five professionals in Data QC. In the second stage, the weights of Data QC activities were elicited from the field professionals. An aggregation model between COQ and Data QC activities has been then proposed to help the practitioners make a resource allocation strategy. DEA (Data Envelopment Analysis) was utilized for locating efficient decision points. The proposed resource allocation model has been validated using the case of Korea national defense information system. This research is unique in that it applies the concept of COQ to the data management for the first time and that it demonstrates a possible contribution to a real world case for budget allocation of national defense information.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2011년도 춘계학술발표대회 논문집
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• pp.63-67
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• 2011

KnowledgThe Korea Institute of Energy Research(KIER) has begun collecting horizontal global insolation data since May, 1982 at different locations. Because of a poor reliability of existing data, KIER's new data will be extensively used by the solar system users as well as by research institutes. But the quality of solar insolation data is not always good. This reports on an attempt to identify systematic error in such data using clear-day analysis for data rehabilitation. Clear-day analysis is successful in uncovering solar insolation data of questionable quality. It is not proven that rehabilitation process can improve the quality of data for daily or monthly means, but it is suggested that the method can be used to improve the quality of data for monthly means of several years for use in many applications of solar energy plarming. Earlier studies finding a maximum ETR of about 0.80 are confirmed.

• 융합보안논문지
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• 제23권2호
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• pp.55-66
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• 2023

최근 전세계적으로 사회 모든 분야에서 인공지능 학습용 데이터에 관한 선행연구를 기반으로, 인공지능 학습용 데이터의 가치를 높이고 고품질 데이터를 확보하고자 하는 움직임이 늘고 있다. 따라서, 고품질 데이터를 확보하기 위한 구축사업에서는 품질관리가 매우 중요하다. 이에, 본 논문에서는 인공지능 학습용 데이터를 구축할 시 고품질데이터 확보를 위한 품질관리와 그에 따른 구축공정별 개선방안을 제시하였다. 특히, 인공지능 학습을 위해 구축되는 비정형데이터는 데이터 품질의 80% 이상이 구축과정에서 결정된다. 본 논문에서는 비정형데이터 이미지/영상데이터에 대한 품질검사를 통해 구축단계에서의 획득, data cleaning, labeling 모델에서 발생된 검사절차 및 문제 요소를 해결함으로써 고품질 데이터 확보 방안을 제시하였으며, 제시한 방안을 토대로 인공지능 학습용 데이터 구축에 참여하는 연구단체와 사업자들에게 데이터의 품질편차를 극복하기 위한 대안이 될 것으로 기대된다.

• 품질경영학회지
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• 제49권1호
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• pp.81-95
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• 2021

Purpose: The purpose of this study was to analysis pattern of nonconforming farmers who is one of the factors of unconformity in residual pesticides. Methods: Pattern analysis of nonconforming farmers were analyzed through convergence of safety data and farmer's DB data. Exploratory data analysis and association rule analysis were used for extracting factors related to unconformity. Results: The results of this study are as follows; regarding the exploratory data analysis, it was found that factors of farmers influencing unconformity in residual pesticides by total 9 factors; sampling time, gender, age, cultivation region, farming career, agricultural start form, type of agriculture, cultivation area, classification of agricultural products. Regarding the association rule analysis, non-conformity association rules were found over the past three years. There was a difference in the pattern of nonconforming farmers depending on the cultivation period. Conclusion: Exploratory data analysis and association rule analysis will be useful tools to establish more efficient and economical safety management plan for agricultural products.