• Korean Journal of Clinical Laboratory Science
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• v.48 no.4
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• pp.348-354
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• 2016

Data stored in hospital information systems has a great potential to improve adequacy assessment and quality management. Moreover, an establishment of a data warehouse has been known to improve quality management and to offer help to clinicians. This study constructed a data mart that can be used to analyze antibiotic usage as a part of systematic and effective data analysis of infection control information. Metadata was designed by using the XML DTD method after selecting components and evaluation measures for infection control. OLAP-a multidimensional analysis tool-for antibiotic usage analysis was developed by building a data mart through modeling. Experimental data were obtained from data on antibiotic usage at a university hospital in Cheonan area for one month in July of 1997. The major components of infection control metadata were antibiotic resistance information, antibiotic usage information, infection information, laboratory test information, patient information, and infection related costs. Among them, a data mart was constructed by designing a database to apply antibiotic usage information to a star schema. In addition, OLAP was demonstrated by calculating the statistics of antibiotic usage for one month. This study reports the development of a data mart on antibiotic usage for infection control through the implementation of XML and OLAP techniques. Building a conceptual, structured data mart would allow for a rapid delivery and diverse analysis of infection control information.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.1136-1138
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• 2014

Recently, predicting personality with the help of smartphone usage is become very interesting and attention grabbing topic in the field of research. At present there are some approaches towards detecting a user's personality which uses the smartphones usage data, such as call detail records (CDRs), the usage of short message services (SMSs) and the usage of social networking services application. In this paper, we focus on the predicting user attitude based on MBTI theory by using their smartphone usage data. We used Naïve Bayes and SVM classifier for classifying user personalities by extracting some features from smartphone usage data. From analysis it is observed that, SVM classifier works well as compared to Naïve Bayes.

• Health Policy and Management
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• v.33 no.3
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• pp.295-310
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• 2023

Background: The long-term care (LTC) group has higher rates of chronic disease and disability registration compared to the general older people population. There is a need to provide integrated medical services and care for LTC group. Consequently, this study aimed to identify medical usage patterns based on the ratings of LTC and the characteristics of benefits usage in the LTC group. Methods: This study employed the National Health Insurance Service Database to analyze the effects of demographic and LTC-related characteristics on medical usage from 2015 to 2019 using a repeated measures analysis. A longitudinal logit model was applied to binary data, while a linear mixed model was utilized for continuous data. Results: In the case of LTC ratings, a positive correlation was observed with overall medical usage. In terms of LTC benefit usage characteristics, a higher overall level of medical usage was found in the group using home care benefits. Detailed analysis by medical institution classification revealed a maintained correlation between care ratings and the volume of medical usage. However, medical usage by classification varied based on the characteristics of LTC benefit usage. Conclusion: This study identified a complex interaction between LTC characteristics and medical usage. Predicting the requisite medical services based on the LTC rating presented a challenge. Consequently, it becomes essential for the LTC group to continuously monitor medical and care needs, even after admission into the LTC system. To facilitate this, it is crucial to devise an LTC rating system that accurately reflects medical needs and to broaden the implementation of integrated medical-care policies.

• KIISE Transactions on Computing Practices
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• v.22 no.6
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• pp.253-267
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• 2016

The world's major cities operate public rental bicycle systems to complement the existing problems of public transport in the city. Disclosing the rental history data in Daejeon has opened new analytical possibilities. In this paper, we proposed a method to analyze the data using the visualization. We found a positional feature of the station according to the bicycle usage. In addition, we examined the bicycle usage patterns according to the time/day/month. On the other hand, the usage patterns between each of the bicycle stations were identified through a path analysis. The specific objectives were identified through each stop destination ratio analysis. Based on these data, we suggest a direction of Daejeon public bicycle rental system development.

• Journal of Korea Multimedia Society
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• v.22 no.3
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• pp.366-373
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• 2019

The development of monitoring and analysis systems to increase productivity while saving energy is needed as a method to reduce huge amount of energy consumed in the process of producing large forged products. In this paper, we propose a system to monitor and analyze energy usage in real-time collected from gas-meter, wattmeter, and thermometer based on IoT installed in forging factories. The system consists of a data collection server for collecting and processing data from IoT- based platform and existing SCADA equipment and ERP/MES system in forging factories, and an application server for providing services to users. To develop the system, the overall system structure is logically diagrammed, and the databases configuration and implementation modules to efficiently store and manage data are presented. In the future, the system will be utilized to reduce energy consumption by analyzing energy usage pattern and optimizing process works with real-time energy usage and production process data for each facility.

• The Journal of Bigdata
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• v.2 no.2
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• pp.105-114
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• 2017

Today, many services are supported on the web sites. Analysis of usage patterns of web site visitors is very important to optimize the use and efficiency of the web sites. In this study, analysis of usage patterns and comparative analysis of user groups were conducted by analyzing web access log provided by BPI Challenge 2016. This data provides access logs to the web site in the IT system of a Dutch Employee Insurance Agency (UWV). The customer information, and the click data describing the customers' behavior when using the agency's web site. In this study, we use process mining techniques to analyze the usage patterns of customers and the characteristics of customer groups, and ultimately improve the service quality of customers using web services.

• Journal of Korean Society for Quality Management
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• v.35 no.1
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• pp.136-141
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• 2007

For many products failures depend on age and usage and, in this case, failures are random points in a two-dimensional plane with the two axes representing age and usage. Models play an important role in decision-making. In this research, an accelerate failure test (AFT) model is proposed to deal with the two-dimensional data. The parameters are proposed to be estimated through maximum likelihood estimators.

• Journal of Family Resource Management and Policy Review
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• v.1 no.1
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• pp.57-70
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• 1997

The purpose of this study is to analyze housewives’ cognition, usage, and management behavior of credit cards according to home management behavior pattern. The sample of this study was selected from the housewives’ living in Seoul who has credit cards. After class-analyzation of the sample, 523 out of 612 repondents were finally selected data. The data were analyzed by the statical methods such as frequency, mean, percentile, Factor Analysis, t-test, ANOVA, Duncan’s multiple range test, Person’s Correlation, and Multiple Regression Analysis through the SAS program package. The major findings of this study are as followers : First, housewives’ usage and management behavior of credit cards was a positive relationship between home management behavior pattern. Second, housewives’ cognition of credit cards showed a significant positive relationship with usage and management behavior of credit card. Third, the most influencial variables on housewives’usage and management behavior of credit cards were their value and home management behavior pattern.

• Asia pacific journal of information systems
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• v.33 no.4
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• pp.934-956
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• 2023

This study focuses on the adoption of Healthcare Information System (HIS) in India's healthcare services, which has led to an increased use of HIS software for managing patient information in hospitals. The study aims to evaluate the factors that influence hospital workers' satisfaction with HIS usage and its impact on their intention to continue in the use of HIS. Primary data was collected through a survey questionnaire from 265 hospital workers. A new framework was developed, and Structural Equation Modeling (SEM) was used for analysis. Sensitivity analysis was also conducted on demographic data using an Artificial Neural Network (ANN) approach. The results indicated that all hypotheses were significant (p < 0.05). Effort expectancy was the most significant factor influencing hospital workers' satisfaction (p < 0.01). Sensitivity analysis showed that education (Model-A) and experience in use of HIS (Model-B) were the most important factors. The study contributes by proposing a new theoretical framework and extending the previous research on HIS usage satisfaction. Overall, the study highlights the importance of easiness and usefulness in predicting HIS usage satisfaction.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.417-423
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• 2016

Recently smartphone has propagated all age groups rapidly due to the fact that the advance of media and the increase of smartphone penetration. In South Korea, high-school students' smartphone retention rate is 90.2%. This study proceed an analysis of high school students' smartphone usage by a feasibility study, and also by recording the log data of actual usage patterns of smartphone applications. The feasibility study investigates subject's smartphone usage, and the log data analysis measures the accurate usage recorded for about three months. We compared the feasibility study and the log data for the daily smartphone usage, and investigated the change of usage pattern during the weekdays, weekends, and the before, during and after exams. High-school students are unique group in capital area. As a result, we found that high-school students' smartphone usage pattern in capital area has not affected by weekday or weekend but has affected by the before, during and after exams.