• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.127-139
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• 2010

Customer behavior pattern discovery is the fundament for conducting customer oriented services and the services management. But, the composition, need, interest and experience of customers may be continuously changing, thereof lead to the difficulty in refining a stable description of their consistent behavior pattern. This paper presented a new method for the behavior pattern discovery from a changing collection of customers. It was originally inspired from the swarm intelligence of ant colony. By the adaptive clustering, some typical behavior patterns which reflect the characteristics of related customer clusters can extracted dynamically and adaptively.

• 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.

• Journal of the Korean Home Economics Association
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• v.25 no.2
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• pp.89-107
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• 1987

This study aimed at providing basis data to understand homemanagement by patterning of the life styles of housewives and by analysing the disposition of homemanagement behavior and characterstics of the behavior pattern to the difference of the life style patterns of housewives. The result can be summarized as follows: 1. By utilizing factor analysis and cluster analysis, the life style types of housewives were classified into 4 types, namely, self-fulness type (28.4%), negative stagnation type (22.7%), fashion-convenience type (23.3%), innovation-urging type (25.6%). 2. The life style types of housewives which were classified, differed significantly according to all of the demographic variables except family size variable. 3. The disposition of homemanagement behavior which were classified into inner-directed type and other-directed type, differed significantly according to the husband's age variable. 4. The patterns of homemanagement behavior which were classified into market pattern and service pattern, differed significantly according to the all of the demographic variables. 5. The disposition and the pattern of homemanagement behavior differed significantly according to the life style types of housewives.

• Nutrition Research and Practice
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• v.10 no.3
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• pp.305-312
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• 2016

BACKGROUND/OBJECTIVES: As the prevalence of chronic diseases has risen, the need for straightforward diagnostic tools for monitoring nutrition status to improve nutrition counseling and disease prevention has likewise increased. This study developed an easily usable dietary behavior pattern diagnosis checklist and investigated its correlation with dietary quality index. SUBJECTS/METHODS: A draft dietary pattern evaluation tool was generated by analyzing previous studies. The draft questionnaire comprised 61 questions for assessing dietary habits. A survey was administered to 320 adults (19 to 64 years old) using the dietary pattern evaluation tool and 24-hour-recall method between March and May of 2014 in Jeonbuk province and the metropolitan area. Principal component analysis with varimax rotation was performed to identify dietary behavior patterns. Nutritional analysis was conducted using CAN-Pro 4.0, and the Diet Quality Index-International (DQI-I) was calculated to assess dietary quality. The correlation between dietary pattern scores and DQI-I scores was also analyzed. RESULTS: The factor analysis resulted in a total of 34 questions mapped to four main dietary behavior patterns: "high fat and calorie" pattern (12 questions), "overeating/binge" pattern (nine questions), "dietary impulse" pattern (eight questions), and "unbalanced food intake" pattern (five questions). The four dietary behavior patterns were negatively correlated with DQI-I adequacy and total scores (P < 0.01). CONCLUSIONS: The dietary pattern evaluation tool developed in this study can be used to diagnose a client's dietary behavior problems and is available as a nutrition counseling tool in the field.

• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.73-88
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• 2022

This exploratory study aims to understand the potential of log data analysis and expand its utilization in user research methods. Transaction log data are records of electronic interactions that have occurred between users and web services, reflecting information-seeking behavior in the context of digital libraries where users interact with the service system during the search for information. Two ways were used to analyze South Korea's National Digital Science Library (NDSL) log data for three days, including 150,000 data: a log pattern analysis, and log context analysis using statistics. First, a pattern-based analysis examined the general paths of usage by logged and unlogged users. The correlation between paths was analyzed through a χ² analysis. The subsequent log context analysis assessed 30 identified users' data using basic statistics and visualized the individual user information-seeking behavior while accessing NDSL. The visualization shows included 30 diverse paths for 30 cases. Log analysis provided insight into general and individual user information-seeking behavior. The results of log analysis can enhance the understanding of user actions. Therefore, it can be utilized as the basic data to improve the design of services and systems in the digital library to meet users' needs.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.570-592
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• 2019

The Event-B design pattern is an excellent way to quickly develop a formal model of the system. Researchers have proposed a number of Event-B design patterns, but they all lack formal behavior semantics. This makes the analysis, verification, and simulation of the behavior of the Event-B model very difficult, especially for the control-intensive systems. In this paper, we propose a novel method to transform the Event-B synchronous control flow design pattern into the labeled transition system (LTS) behavior model. Then we map the design pattern instantiation process of Event-B to the instantiation process of LTS model and get the LTS behavior semantic model of Event-B model of a multi-level complex control system. Finally, we verify the linear temporal logic behavior properties of the LTS model. The experimental results show that the analysis and simulation of system behavior become easier and the verification of the behavior properties of the system become convenient after the Event-B model is converted to the LTS model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.277-278
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• 2008

• Journal of Korea Game Society
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• v.16 no.4
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• pp.35-44
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• 2016

To apply the player's experiences to the design process of the game levels set by the developer, gameplay behavior analysis is needed. The player's behavior which is different by how much he got experiences from the play has generally been studied by one computational approach based on numerical data and the other HCI(human-computer interaction) approach through heuristic analysis. For the analysis of the player's behavior with the level design patterns in FPS(first-person shooter) games, in this paper those methods are used to code 12 main types of action, which in turn is simply categorized into 5 kinds of behavior pattern. Along with it, an optimized visualization is proposed to intuitively compare the flow of behavior pattern with the time of playing game.

• Korean Journal of Community Nutrition
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• v.22 no.6
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• pp.475-484
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• 2017

Objectives: The study was conducted to investigate the relationship between several stress measures in everyday life, emotional eating behavior, and dietary pattern (snacks, fatty foods, sweet beverages, fruits and vegetables) in school-aged children. Methods: One hundred and ninety-four students of an elementary school located in Seongnam City participated in the study. The students responded to the survey questionnaire by self-report, which consisted of items regarding general characteristics, height, weight, dietary habits, frequency of consuming healthy (fruits and vegetables) and unhealthy foods (snacks, fatty foods, and sweet beverages), emotional eating behavior, and daily stress. Correlational analysis was performed to examine the relationship between stress, emotional eating behavior, and dietary pattern, and Poisson and logistic regression analyses were conducted to investigate the effects of stress on dietary pattern. Results: Positive correlations were found between all stress factors and emotional eating behavior and between the friend and personal factor (one of the stress factors) and the consumption of sweet beverages. The frequency of consuming sweet beverages was 2.6 times higher in the high stress group than in the low stress group (95% CI). Conclusions: Children's daily stress was associated with emotional eating behavior and undesirable dietary pattern such as consumption of sweet beverages.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2589-2599
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• 2012

This study was intended to assess fatigue symptoms of nurses working for general hospitals and to reveal its association with the type A behavior pattern. The self-administered questionnaires were given to 306 nurses employed in general hospitals from May, 1st to June, 30th 2011. As a results, The proportion of behavior pattern was 50.7%, of type A, and 49.3% of type B. The distribution of fatigue symptoms was 76.8% of normal group, and 23.2% of high risk group. In terms of the distribution of fatigue symptoms according to the type A behavior pattern, the normal group of fatigue symptoms was higher in the type B behavior pattern group, but the high risk group of fatigue symptoms was higher in the type A behavior pattern group. The type A behavior pattern was significant positive correlation with fatigue symptoms. In logistic regression analysis, the adjusted odds ratio of the high risk fatigue symptoms were significantly increased in the group of type A behavior pattern than type B behavior pattern. As a conclusion, fatigue symptoms was higher in the group of type A behavior pattern than the group of type B behavior pattern. Hereafter, the sustainable study would require that the relation between type A behavior pattern and fatigue symptoms to the various study subjects.