• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.708-720
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• 1993

The new preventive control mechanisms for traffic management in BISDN/ATM networks can be divided into Connection Admission Control(CAC), Usage Parameter Control (UPC), and Priority Control. Of these mechanism, Usage Parameter Control continuously monitors the parameters admitted in the network's entry point to guarantee quality of service of connections already admitted. Upon detecting traffic that violates the negotiated parameter, it takes the necessary control measures to prevent congestion. Among these traffic control methods, this paper focuses on the Usage Parameter Control method, and proposes and designs GRACE-LB(Guaranteed Rate Acceptance & Control Element-using Leaky Bucket) which improves upon existing UPC models. GRACE-LB modifies the previous LB model by eliminating the cell buffer, dividing the token Pool into two pools, Long-term pool, Short-term pool, and changing the long-term token generating form using 'Cycle Token' into the same bursty form as the traffic source. Through this, GRACE-LB achieves effective control of the Average Bit Rate(ABR) and burst duration of bursty multimedia traffic which previous LB models found difficult to control. Also, since GRACE-LB can e implemented using only simple operations and there are no cell buffers in it, it has the merit of being easily installed at any place.

• Korean Journal of Ecology and Environment
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• v.49 no.3
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• pp.208-214
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• 2016

We analyzed research trend and public perception related with tap water to identify major factors affecting low consumption of tap water. 805 research articles were collected for text mining analysis and 1,000 on-line questionnaires were surveyed to find social variables influencing tap water intake. Based on the word network analysis, research topics were divided into 4 major categories, 1) drinking water quality, 2) water fluoridation, 3) residual chlorine, and 4) micro-organism management. Compared with these major research topics, scientific studies of drinking behavior, or social perception were rather limited. 22.4% of total respondents used tap water as drinking water source, and only 1% drank tap water without further treatments (i.e. boiling, filtering). Experience of quality control report (B=0.392, p=0.046) and level of policy trust (B=1.002, p<0.0001) were influential factors on tap water drinking behavior. Age (B=0.020, p=0.002) and gender (B= - 1.843, p<0.0001) also showed significant difference. To increase the frequency of drinking the tap water by social members, the more scientific information of tap water quality and the water policy management should be clearly shared with social members.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.403-409
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• 2016

This study is a food shopping experience of omni-channel. Food threats and healthy living concerns bring different channels in led to increase reasonable way such as various demand. Omni-channels should be premised on understanding customer behavior as well as empirical user types in which considerations including the value of experience and understanding consumer behavior. Online survey result showed that, (1)offline food shopping, major retail store with quality, buy fresh food directly 2~3 times a month (2)online food shopping, e-commerce site with costs, buy fruits & nuts 2~3 times a month. After in-depth interview with eight high quality participants, I analyzed needs for food shopping experience in regard to the four steps food purchasing journey then derived a persona with integral value 'health' and 'diet'. It is classified into two types. One is the primary persona, family and health oriented, considering household money 'saving', and other is secondary persona, work and personal oriented, looking forward to 'automatic supply'. The result of this study provided an insight that help us explore ways to resolve function and services in the context of a healthy and balanced diet for improving food shopping experience of omni-channel.

• The Journal of Engineering Research
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• v.5 no.1
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• pp.17-35
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• 2004

Svg(Scalable Vector Graphics) is xml graphic standard recommended by E3C as a language based on xml to express two-dimension graphic. Svg can accommodate all Xml's patency and advantage of interoperability, and can used as various web applications being combined with other xml language. In addition, Svg can be applied to the fields of electronic commerce, geographical information, computer education and advertisement because it can produce high quality of dynamic from real-time data. SVG's application can be enhanced by linking with database. In this paper, we discuss how Svg can be utilized in theater reservation system, not just explaining svg's meaning or ability. Svg added graphic advantage in addition to xml's advantage. This means that svg retains not only graphic element but also xml's softness. It becomes easier to designate seats and add them. Current reservation system provided in general only information on time and price for a ticket, but the system using SVG in this paper provides additional information on position, price, cancellation and purchase availability of seat.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Global Scholars of Marketing Science
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• v.19 no.1
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• pp.23-35
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• 2009

A hypermarket as the one of the most globally standardized retailing format is also the type of store among various types of stores that the most active in expanding into other foreign markets. Recently, as several Korean retailing companies start to penetrate into Chinese market they differentiate themselves with modern facilities and customers service oriented high-end concept. China and Korea as Far East Asian countries share many common values, however precise and careful analysis should be carried out since there may also be critical differences in socio-economic aspects as well as in consumption patterns due to the level of development stages of retail industry among two countries. Even though precise and careful study is crucial on Chinese retailing market and consumers, none of researches and studies on 'how the quality of service dimensional structure is different between Korea and China', and 'what will be the most important and influential service dimensional factors for Chinese consuers compared to the hypermarkets customers in Korea' in order to improve the level of Chinese consumers satisfaction' have been fulfilled At this point of view, this study uses KD-SQS (Rho Eun Jung & Sir Yong Gu, 2008) which is a measure of Korean hypermarkets service quality to set up a hypothesis on Korean and Chinese consumers, and an empirical analysis is conducted. We try to get the answers about how the comparative importance of Service quality dimensions which decides the level of customer satisfaction is different depending on the cultural dimensions and socio-economic factors among two countries, Korea and China. Based upon the results, we try to give a valuable suggestion of what service dimensional factors should be reinforced to improve the level of CS in Chinese retailing market. Hypotheses for this study are as follows : H1. Each dimension of Service Quality significantly affects the level of CS H2. The effect of 'Basic Benefit' in service quality dimensions on the level of CS is greater in China than in Korea H3. The effect of 'Promotion' in service quality dimensions on the level of CS is greater in China than in Korea H4. The effect of 'Physical Aspects'in service quality dimensions on the level of CS is greater in Korea than in China. H5. The effect of 'Personal Interaction' in service quality dimensions on the level of CS is greater in China than in Korea H6. The effect of 'Policy' in service quality dimensions on the level of CS will be greater in Korean than in China H7. The effect of additional convenience in service quality dimensions on the level of CS will be greater in Korean than in China. More than 1,100 data were collected directly from the surveys of Chinese and Korean consumers in order to verify the hypotheses above. In Korea, stores which have floor space of over $9,000m^2$and opened later than year 2000 were selected for the samples, and thus Gayang, Wolgye, Sangbong, Eunpyeong, Suh-Suwon, Gojan stores and their customers were surveyed. In China, notable differences in the income levels and consumer behaviors between cities and regions were considered, and thus the research area was limited to the stores only in Shanghai. 6 stores which have the size of over $6,000m^2$ and opened later than 2000, such as Ruihong, Intu, Mudanjang, Sanrin, Raosimon, and Ranchao stores were selected for the survey. SPSS 12.0 and AMOS 7.0 were used as statistical tools, and exploratory factor analysis, confirmatory factor analysis, and multi-group analysis were conducted. In order to carry out a multi group analysis that decides whether the structure variables which shows the different effects of 6 service dimensions in Korean and Chinese groups is statistically valid, configural invariance, metric invariance, and structural invariance are tested in order. At the results of the tests, 3 out of 7 hypotheses were supported and other 4 hypotheses were denied. According to the study, 4 dimensions (Basic Benefit, Physical Environment, Policy, and additional convenience) were positively correlated with CS in Korea, and 3 dimensions (i.e. basic benefit, policy, additional convenience) were significant in China. However, the significance of the service-dimensions was turned out to be partially different in Korea and China. The Basic Benefit is more influential in deciding the level of CS in china than Korea, however Physical Aspect is more important factor in Korea. 'Policy dimension' did not make significant difference between two countries. In the 'additional convenience dimension', the differences in 'socio-economic factors' than in'cultural background' were considered as more important in Chinese consumers than Korean. Overall, the improvement of Service quality will be crucial factors to increase the level of CS in Chinese market same as Korean market. In addition, more emphases need to be placed on the service qualities of 'Basic Benefit' and 'additional convenience' dimensions in China. In particular, 'low price' and 'product diversity' that constitute 'Basic Benefit' are proved to be comparatively disadvantageous and weak points of Korean companies compared to global players, and thus the prompt strengthening those dimensions would be urgent for Korean retailers. Moreover, additional conveniences such as various tenants and complex service and entertaining area will be more important in China than in Korea. Besides, Applying advanced Korean Hypermaret`s customer policy to Chinese consumers will help to get higher reliability and to differentiate themselves to other competitors. However, as personal interaction, physical aspect, promotions were proved as not significant for the level of CS in China, Korean companies need to reconsider the priority order of resource allocations when they tap into Chinese market.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.611-635
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• 2014

Information systems (IS) have become ubiquitous and changed every aspect of how people live their lives. While some IS have been successfully adopted and widely used, others have failed to be adopted and crowded out in spite of remarkable progress in technologies. Both the technology acceptance model (TAM) and the IS Success Model (ISSM), among many others, have contributed to explain the reasons of success as well as failure in IS adoption and usage. While the TAM suggests that intention to use and perceived usefulness lead to actual IS usage, the ISSM indicates that information quality, system quality, and service quality affect IS usage and user satisfaction. Upon literature review, however, we found a significant void in theoretical development and its applications that employ either of the two models, and we raise research questions. First of all, in spite of the causal relationship between intention to use and actual usage, in most previous studies, only intention to use was employed as a dependent variable without overt explaining its relationship with actual usage. Moreover, even in a few studies that employed actual IS usage as a dependent variable, the degree of actual usage was measured based on users' perceptual responses to survey questionnaires. However, the measurement of actual usage based on survey responses might not be 'actual' usage in a strict sense that responders' perception may be distorted due to their selective perceptions or stereotypes. By the same token, the degree of system quality that IS users perceive might not be 'real' quality as well. This study seeks to fill this void by measuring the variables of actual usage and system quality using 'fact' data such as system logs and specifications of users' information and communications technology (ICT) devices. More specifically, we propose an integrated research model that bring together the TAM and the ISSM. The integrated model is composed of both the variables that are to be measured using fact as well as survey data. By employing the integrated model, we expect to reveal the difference between real and perceived degree of system quality, and to investigate the relationship between the perception-based measure of intention to use and the fact-based measure of actual usage. Furthermore, we also aim to add empirical findings on the general research question: what factors influence actual IS usage and how? In order to address the research question and to examine the research model, we selected a mobile campus application (MCA). We collected both fact data and survey data. For fact data, we retrieved them from the system logs such information as menu usage counts, user's device performance, display size, and operating system revision version number. At the same time, we conducted a survey among university students who use an MCA, and collected 180 valid responses. A partial least square (PLS) method was employed to validate our research model. Among nine hypotheses developed, we found five were supported while four were not. In detail, the relationships between (1) perceived system quality and perceived usefulness, (2) perceived system quality and perceived intention to use, (3) perceived usefulness and perceived intention to use, (4) quality of device platform and actual IS usage, and (5) perceived intention to use and actual IS usage were found to be significant. In comparison, the relationships between (1) quality of device platform and perceived system quality, (2) quality of device platform and perceived usefulness, (3) quality of device platform and perceived intention to use, and (4) perceived system quality and actual IS usage were not significant. The results of the study reveal notable differences from those of previous studies. First, although perceived intention to use shows a positive effect on actual IS usage, its explanatory power is very weak ($R^2$=0.064). Second, fact-based system quality (quality of user's device platform) shows a direct impact on actual IS usage without the mediating role of intention to use. Lastly, the relationships between perceived system quality (perception-based system quality) and other constructs show completely different results from those between quality of device platform (fact-based system quality) and other constructs. In the post-hoc analysis, IS users' past behavior was additionally included in the research model to further investigate the cause of such a low explanatory power of actual IS usage. The results show that past IS usage has a strong positive effect on current IS usage while intention to use does not have, implying that IS usage has already become a habitual behavior. This study provides the following several implications. First, we verify that fact-based data (i.e., system logs of real usage records) are more likely to reflect IS users' actual usage than perception-based data. In addition, by identifying the direct impact of quality of device platform on actual IS usage (without any mediating roles of attitude or intention), this study triggers further research on other potential factors that may directly influence actual IS usage. Furthermore, the results of the study provide practical strategic implications that organizations equipped with high-quality systems may directly expect high level of system usage.