• Journal of Digital Convergence
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• v.5 no.1
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• pp.117-130
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• 2007

This paper examines whether there exists discrimination of the information usage pattern between tenants of permanent and temporary public rental housing in Korea. Moreover, we focus to examine on that matters between two groups, $7{\sim}12$ pyeongs and $11{\sim}24$ pyeongs that indicate permanent and temporary public rental housing respectively. The results derived in this paper can be summarized as follows: there exists statistical significance in using computer, internet, communication and broadcasting, on the other hand, insignificance in using satellite communication and telephone between them. The implication of this results shows that as widen income gap, also widen gaps in using computer and internet between them. Thus government public rental housing policy should focus to make narrow income gap to diminish information gap between those groups.

• Survey Research
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• v.12 no.3
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• pp.123-142
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• 2011

This study examines how the family background and educational aspiration of adolescents affect the usage pattern of internet among adolescents. Recently, the focus of studies on digital divides shifts from the gap in the access to the internet to the difference in usage pattern of internet. Building on these studies, this study deals with the concerns that the difference in usage pattern of internet among adolescents potentially lead to the reproduction of social inequality across the generations. The analysis of the Korean Youth Panel Study reveals that the higher educational attainment and higher income level of parents, the children tend to spend more time in searching with the internet and spend less time in doing the internet game. The level of educational aspiration exerts similar effects on internet use of adolescents. The results also show that the effects of educational apsiration is larger among older adolescents. The implications and limitations of this study are discussed.

• Proceedings of the Korea Technology Innovation Society Conference
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• 1999.11c
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• pp.137-141
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• 1999

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2490-2506
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• 2022

In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.143-146
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• 2004

With the rapid growth of e-commerce, marketers are able to observe not only purchasing behavior on what and when customers purchased, but also the individual Web usage behavior that affect purchasing. The richness of this information has the potential to provide marketers with an in-depth understanding of customer. Using commonly available Web log data, this paper examines Web usage behaviors at the individual level. By decomposing the buying process into a pattern of visits and purchase conversion at each visit, we can better understand the relationship between Web usage behavior and purchase decision. This allows us to more accurately forecast a shopper's future purchase decision at the site and hence determine the value of individual customers to the siteAccording to our research, not only information seeking behavior but also visiting duration of a customer and participative behavior such as participation in event should be considered as important predicators of purchase decision of customer in a cosmetic internet shopping mall.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1712-1732
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• 2011

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.6
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• pp.907-917
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• 2000

Although interest in the potential and actual usage of the Internet as a transaction medium is increasing, the market for fashion merchandise on the Internet in Korea has yet to take off. As the Internet environment is expected to bring about a transformation of conventional consumer buying behavior, the purpose of this exploratory research is to investigate the buying behavior of fashion products on the Internet to identify relevant concepts and generate hypotheses for further empirical research. The research methods selected for the study were observation and in-depth interview. Twelve subjects who had purchased fashion products on the Internet were selected and interviewed. Those who could not participate in face-to-face in-depth interviews because of the geographic locations were interviewed on-line. The results are as follows: First, subjects went through the stages of shopping motivation stage, site choice behavior stage, in-site behavior stage, and postpurchase behavior stage. Second, a model was extracted for each shopping stage, and a final model was completed based on comparisons with the participants processes. The information content of each phase was discussed. Finally, each participant was classified using their purchasing process, revealing six possible mixed usage patterns of the Internet marketing system and the traditional marketing system.

• Korean journal of communication and information
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• v.30
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• pp.35-87
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• 2005

This research explores the empirical confirmation of the Internet development including from the period of innovation to the time of social-cultural use in today. The research focused on how tradition news media rover about the Internet developed from early to today, and understanding the media characteristics on the each stages from news frame. The research Is designed to conduit content analysis from 1989 to 2004, then this research is divided four(4) stages of the Internet development: innovation, diffusion, commercial usage, social-cultural usage. The results shows that there are significant different coverage by the stages. First of all, the news coverage pattern shift from technology focused on early stages to social usage focused on the later stages. This research confirms that the ratio of the seriation(technology) coverage defined when social usage is increased, on the other hand skeumorphs(social usage and content) coverage is increased in the commercial and social usage stages. This coverage pattern among news media does not such a big different and there is no competitive coverage. Moreover, the news coverage shifted from thematic coverage on early stages to episodic coverage while the number of usage increasing. In addition, the tone of coverage has not been changed significantly.