• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.89-98
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• 2005

Recently the construction industry in Korea is facing problems such as low productivity, contraction of the domestic construction market, growing competition, and so on. To enhance the competitiveness continuously through efficiency in this business environments, construction companies need to make efforts to measure and accumulate performance data based on the strategic factors. When analysing performance of construction projects, the unique characteristics of each project should be considered properly, by which the managers can identify current status of project in various perspectives. This study proposes the performance analysis system using the concepts of balanced scorecard and data warehouse technology. The suggested system provides the management with the flexibility in analyzing performance data by applying the pre-defined key performance indicators and the function of multi-dimensional analysis.

• Journal of Home Health Care Nursing
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• v.28 no.2
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• pp.135-143
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• 2021

Purpose: This study aimed to provide basic data for the development of education programs which improve the nurses' infection control performance by investigating the knowledge, performance, and educational needs of infection control among nurses in long-term care hospitals, and analyzing the relationship between these parameters. Methods: This was a descriptive study. A self-reported questionnaire was provided to 153 nurses in 210 long-term care hospitals on Jeju Island. Their knowledge, performance, and educational needs of infection control, data were analyzed using SAS Window(ver. 9.4), t-test, Wilcoxon rank-sum test, one-way ANOVA, Scheffe test, and Pearson's correlation coefficient. Results: Both knowledge (r=0.16, p=.042) and performance (r=0.52, p<.001) of infection control had positive correlations with the educational needs of the infection control. Conclusion: The higher the knowledge of infection control was, the higher the educational needs of the nurses were. However, knowledge of infection control did not correlate with performance of infection control. Therefore to increase the knowledge and performance of infection control, infection control education programs should suit the educational needs and the actual conditions of long-term care hospitals.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1271-1277
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• 2018

This paper deals with a voltage sag assessment based on a voltage monitoring program. The voltage sag performance at a specific site can be evaluated by analyzing voltage monitoring data recorded for a long time period. Although an assessment based on voltage monitoring is an effective way to understand voltage sag performance at a measurement site, the statistical confidence of voltage sag frequency estimation heavily depends on the length of monitoring period and the number of recorded events. Short monitoring period and insufficient recorded data can not provide a reliable assessment result. This paper proposes a compensation assessment method by combining a computer simulation approach for in case that monitoring period and data are not enough for a valid assessment.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9731-9737
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• 2014

Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.408-411
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• 2006

Currently, the nano economy threatens the mass economy. This is based on the internet business models. In the nano business models based on internet, the diversely personalized services are needed. Many researches of the personalization on the web have been studied. The web usage mining using click stream data is a tool for personalization model. In this paper, we propose an internet business model using evolutionary support vector machine and web response survey as a web usage mining. After analyzing click stream data for web usage mining, a personalized service model is constructed in our work. Also, using an approach of web response survey, we improve the performance of the customers' satisfaction. From the experimental results, we verify the performance of proposed model using two data sets from KDD Cup 2000 and our web server.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.73-77
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• 2021

As the big data analysis technologies has been developed worldwide, the importance of asset management for electric power facilities based data analysis is increasing. It is essential to secure quality of data that will determine the performance of the RISK evaluation algorithm for asset management. To improve reliability of asset management, asset data must be preprocessed. In particular, the process of cleaning dirty data is required, and it is also urgent to develop an algorithm to reduce time and improve accuracy for data treatment. In this paper, the result of the development of an automatic cleaning algorithm specialized in overhead transmission asset data is presented. A data cleaning algorithm was developed to enable data clean by analyzing quality and overall pattern of raw data.

• Asian Journal of Innovation and Policy
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• v.5 no.3
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• pp.315-329
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• 2016

Public R&D investment information has diverse implications for researching R&D trends. Also, as it is important for the establishment of R&D policy to grasp the current situation and trends of R&D to improve science and technology level, science and technology information service system, such as NTIS (National Science & Technology Information Service), is operated at a national level in most countries. However, since the data forms provided by current NTIS are raw data, it is necessary to develop the R&D performance indicator or to use additional scientometric methods by analyzing scientific papers or scientific R&D project information for grasping R&D trends or analyzing R&D task results. Thus, this study applied public R&D investment information to investigate and monitor R&D trends in the field of information & communication technology (ICT) of meteorology and climatology by using NTIS data of Korea and NSF (National Science Foundation) data of USA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3182-3202
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• 2015

Flexibly expanding the storage capacity required to process a large amount of rapidly increasing unstructured log data is difficult in a conventional computing environment. In addition, implementing a log processing system providing features that categorize and analyze unstructured log data is extremely difficult. To overcome such limitations, we propose and design a MongoDB-based unstructured log processing system (MdbULPS) for collecting, categorizing, and analyzing log data generated from banks. The proposed system includes a Hadoop-based analysis module for reliable parallel-distributed processing of massive log data. Furthermore, because the Hadoop distributed file system (HDFS) stores data by generating replicas of collected log data in block units, the proposed system offers automatic system recovery against system failures and data loss. Finally, by establishing a distributed database using the NoSQL-based MongoDB, the proposed system provides methods of effectively processing unstructured log data. To evaluate the proposed system, we conducted three different performance tests on a local test bed including twelve nodes: comparing our system with a MySQL-based approach, comparing it with an Hbase-based approach, and changing the chunk size option. From the experiments, we found that our system showed better performance in processing unstructured log data.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.39-48
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• 2020

Data mining techniques have been suggested to find efficiently meaningful and useful information. Especially, in the big data environments, as data becomes accumulated in several applications, related pattern mining methods have been proposed. Recently, instead of analyzing not only static data stored already in files or databases, mining dynamic data incrementally generated in a real time is considered as more interesting research areas because these dynamic data can be only one time read. With this reason, researches of how these dynamic data are mined efficiently have been studied. Moreover, approaches of mining representative patterns such as maximal pattern mining have been proposed since a huge number of result patterns as mining results are generated. As another issue, to discover more meaningful patterns in real world, weights of items in weighted pattern mining have been used, In real situation, profits, costs, and so on of items can be utilized as weights. In this paper, we analyzed weighted maximal pattern mining approaches for data generated incrementally. Maximal representative pattern mining techniques, and incremental pattern mining methods. And then, the application scenarios for analyzing the required commodity patterns in infants are presented by applying weighting representative pattern mining. Furthermore, the performance of state-of-the-art algorithms have been evaluated. As a result, we show that incremental weighted maximal pattern mining technique has better performance than incremental weighted pattern mining and weighted maximal pattern mining.