• Journal of Information Science Theory and Practice
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• v.2 no.2
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• pp.33-47
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• 2014

Mobile devices are the most important equipment for accessing various kinds of services. These services are accessed using wireless signals, the same used for mobile calls. Today mobile services provide a fast and excellent way to access all kinds of information via mobile phones. Mobile service providers are interested to know the access behavior pattern of the users from different locations at different timings. In this paper, we have introduced an associated tree for analyzing user behavior patterns while moving from one location to another. We have used four different parameters, namely user, location, dwell time, and services. These parameters provide stronger frequent accessing patterns by matching joins. These generated patterns are valuable for improving web services, recommending new services, and predicting useful services for individuals or groups of users. In addition, an experimental evaluation has been conducted on simulated data. Finally, performance of the proposed approach has been measured in terms of efficiency and scalability. The proposed approach produces excellent results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8B
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• pp.1150-1158
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• 2010

At present multimedia service composed of voice, data, and video service is prevalent in the Internet. As such, wide-scale penetration of Internet service imposes tremendous pressure to the network infrastructure such as the media servers and links as well as the nodes. In addition, users from a large-scale population require broad bandwidth and high level of QoS. This requires a network with reliable and scalable services to customers, which also necessitates a realistic method for the design of the a media server. In this work, we explore the influence of user's behavior about delay on the performance of media server that takes into account the system and user attributes in a realistic manner. By incorporating user's behavior about the delay-sensitivity, we present an analytic framework for the evaluation of the performance of the media server, via which we illustrate a meaningful intuition in the provision of Internet multimedia service.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.85-96
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• 2022

Personalized recommendation technology is one of the most important technologies in electronic commerce environment. It helps users overcome information overload by suggesting information that match user's interests. In e-commerce environment, both mobile device users and smart device users have risen dramatically. It creates new challenges. Our method suggests product information that match user's device interests beyond only user's interests. We propose a device-centered personalized recommendation method. Our method uses both purchase and share behavior for user's devices interests. Moreover, it considers data type preference for each device. This paper presents a new recommendation method and algorithm. Then, an e-commerce scenario with a computer, a smartphone and an AI-speaker are described. The scenario shows our work is better than previous researches.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.31-36
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• 2016

User authentication is the first step to network security. There are lots of authentication types, and more than one authentication method works together for user's authentication in the network. Except for biometric authentication, most authentication methods can be copied, or someone else can adopt and abuse someone else's credential method. Thus, more than one authentication method must be used for user authentication. However, more credential makes system degrade and inefficient as they log on the system. Therefore, without tradeoff performance with efficiency, this research proposed user's behavior based authentication for secure communication, and it will improve to establish a secure and efficient communication.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.139-146
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• 2008

It is very important to identify useful web user group and induce their behavior pattern in eCRM domain. Inducing user group with a similar inclination, a reliability of user group decreases because there is an uncertainty in online user data. In this paper, we have applied the 2-tier clustering, which uses the outcome of interaction with data from other tiers. Also we propose a method which induces user behavior pattern from a cluster and compare C4.5 with our method.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1726-1732
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal attackers - masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on this, the used pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with the age of the user profile. The performance of the proposed scheme is evaluated by using a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed scheme that considers the age of user profiles.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.315-326
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents a dynamic anomaly detection scheme that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with both the age of the user profile and weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed dynamic scheme that considers the age of user profiles.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.39-40
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• 2011

• Knowledge Management Research
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• v.17 no.1
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• pp.155-174
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• 2016

Recent years, a smartphone has been playing a role of searching, booking, and decision-makings in the tourism industry through tourists' user experiences and engagements. This study investigated the tourists' behavior toward smartphone usage regarding Chinese visitors who were ranked in Korea inbound tourism. This research model formulated two types of usage which identified explorlative use and exploitive use and the antecedent relationships of user empowerment including user competence, user impact, usage meaning, user trust. We analyzed SmartPLS2.0 for the hypotheses and found that first, user competence has a positive influence both of exploitative use and explorative use, second, user impact has a positive influence on exploitative use, but has not influenced on explorative use, third, usage meaning has not influence on exploitive use, but has a postitive influence on explorative use, and finally user trust has influence on both of exploitive and explorative use. Based on the results we found, we suggested Chinese tourists' smartphone usages' behaviors and theoretical and practical implications.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.95-105
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• 2019

A great deal needs to be learned about why and how users participate and consume information on various online sites. The design of socio-technical systems especially for promoting engagement in terms of maximum user participation is both a theoretical and real-world challenge that researchers strive to understand. At present, most of the research on the motives of Internet video users' behavior focuses on the user's "viewing motivation" and "sharing motivation", and lacks the analysis of the factors affecting users' "subscription motivation". This study will attempt to compensate for this gap. Based on the YouTube platform, we take Chinese video users as the research object and uses the "Censydiam user motivation analysis model" to make assumptions about user subscription motivation from the two levels of social needs and personal needs, using regression analysis. Validate the hypothesis and get the influencing factors that may be available in the user's subscription motivation based on the assumptions. Built on survey data from 215 respondents, the study found that Enjoyment, Vitality, Power, and Conviviality are four factors that influence user motivation.