• The Journal of Korean Association of Computer Education
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• v.6 no.1
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• pp.29-39
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• 2003

In this paper we proposed dynamic adaptive model based on discrete probabilistic distribution functions and user's profile for web media systems(web based hypermedia systems). The model represented that the application domain is modelled using a weighted direct graph and the user's behaviour is modelled using a probabilistic approach that dynamically constructs a discrete probability distribution functions. The proposed probabilistic interpretation of the web media structure is used to characterize latent properties of the user's behaviour, which can be captured by tracking user's browsing activity. Using that distribution the system attempts to assign the user to the best profile that fits user's expectations.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.551-561
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• 2016

A user influence discrimination scheme using big data from social networks is needed. In this thesis, we propose a user influence discrimination scheme considering reliability in social networks. The proposed scheme measures reliability scores through social activities and simplifies a social network by collecting only reliable users. It also derives user influence by considering direct and indirect influences that depends on network degree between users. As a result, the proposed scheme improves the expandability of the user influence. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluations in terms of reliability and user influence.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.86-94
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• 2011

In recent years, there has been a rise in epidemiological evidence suggesting the health benefits of a physically active lifestyle. However, it is not always easy for individuals to personally recognize the optimal conditions for exercise and physical activity. Wearable acceleration-based pedometers have become widely used in estimating the amount of physical activity, and to a limited extent, providing information regarding exercise intensity, but they have never been used to assess adaptation to exercise. In order to realize simultaneous activity monitoring for multiple users exercising outdoors, we developed a prototype wireless local area network (WLAN) based system. In our system, a WLAN is deployed outside, and a user wearing a smart phone and monitoring device exercises freely within the coverage area of the wireless network. By doing so, the developed system is able to monitor the activity of each user andmeasures various parameters including those related to exercise adaptation. In a demonstration experiment, the developed system was evaluated and used to monitor users enjoying a Nordic walk, after which users were immediately able to receive their exercise report. In this paper, we discuss the requirements and issues in developing an activity monitoring system and report the findings we obtained through the demonstration experiment.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.529-532
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• 2005

In anomaly intrusion detection, how to model the normal behavior of activities performed by a user is an important issue. To extract the normal behavior as a profile, conventional data mining techniques are widely applied to a finite audit data set. However, these approaches can only model the static behavior of a user in the audit data set This drawback can be overcome by viewing the continuous activities of a user as an audit data stream. This paper proposes a new clustering algorithm which continuously models a data stream. A set of features is used to represent the characteristics of an activity. For each feature, the clusters of feature values corresponding to activities observed so far in an audit data stream are identified by the proposed clustering algorithm for data streams. As a result, without maintaining any historical activity of a user physically, new activities of the user can be continuously reflected to the on-going result of clustering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3328-3349
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• 2020

With rapid development of ubiquitous computing and location-based services (LBSs), human trajectory data and associated activities are increasingly easily recorded. Inappropriately publishing trajectory data may leak users' privacy. Therefore, we study publishing trajectory data while preserving privacy, denoted privacy-preserving activity trajectories publishing (PPATP). We propose S-PPATP to solve this problem. S-PPATP comprises three steps: modeling, algorithm design and algorithm adjustment. During modeling, two user models describe users' behaviors: one based on a Markov chain and the other based on the hidden Markov model. We assume a potential adversary who intends to infer users' privacy, defined as a set of sensitive information. An adversary model is then proposed to define the adversary's background knowledge and inference method. Additionally, privacy requirements and a data quality metric are defined for assessment. During algorithm design, we propose two publishing algorithms corresponding to the user models and prove that both algorithms satisfy the privacy requirement. Then, we perform a comparative analysis on utility, efficiency and speedup techniques. Finally, we evaluate our algorithms through experiments on several datasets. The experiment results verify that our proposed algorithms preserve users' privay. We also test utility and discuss the privacy-utility tradeoff that real-world data publishers may face.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.291-299
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• 2012

Ubiquitous computing is a technique to provide users with appropriate services, collecting the context information in somewhere by attached sensor. An intelligent system needs to automatically update services according to the user's various circumstances. To do this, in this paper, we propose a design of context ontology, trigger rule for mining service pattern related to users activity and an active mining architecture integrating trigger system. The proposed system is a framework for active mining user activity and service pattern by considering the relation between user context and object based on trigger system.

• Journal of the Ergonomics Society of Korea
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• v.29 no.6
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• pp.843-851
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• 2010

This study aims to systematically develop a usability evaluation model using the Structural Equation Model (SEM) from experiment of usability on using vehicle. Vehicle developers have been adding many functions for enhance the user satisfaction. But it will be made the trade-off problem of usability and design elements of vehicle interior from attempt to make best usability satisfaction in a restricted space. To solve the trade-off problem, we set a new solution criterion from usability evaluation model. The usability experiment is based on major activity pool from derived user's acts pattern in vehicle for make more accurate usability evaluation model. And this model was built with twenty-nine measurement variables for the evaluation of usability of vehicle user. As a result, the proposed SEM model showed statistical significance as well as a high level of R Square (0.7144). This model shows the relationships of detailed usability and design elements. According to the result, this study introduces the criterion to secure the best satisfaction of usability and design elements.

• The Journal of Information Systems
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• v.16 no.4
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• pp.75-106
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• 2007

The purpose of this research is to identify a mediating effect of change management activity(CMA) on the relationships between the role of participants of ERP implementation and system performance. In this paper the role of participants are composed of top's management, user's participation, project manager's skill. Also this research organized ERP system performance into the three dimensions(adaptation to the end-user's ERP system, system usage, individual's performance). The major results of this research can be summarized as follow; project manager's role, top management's involvement, and users' participation in ERP project directly affect CMA of system implementation process and have influence system performance through CMA too. CMA also has a positive effect on a series of system performance, that is, adaptation to the end-user's ERP system, system usage, and individual's performance. In conclusion, the role of participants have an considerable influence upon system performance through CMA. This research makes certain that a mediating effect of CMA between the role of participants and system performance exists. Therefore, the companies implementing ERP must consider CMA.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.3
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• pp.139-147
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• 2009

This paper proposed the context awareness reasoning system to provide the personalized services dynamically in a ubiquitous mobile environments. The proposed system is designed to provide the personalized services to mobile users and consists of the context aggregator and the knowledge manager. The context aggregator can collect information from networks through Open API Gateway as well as sensors in a various ubiquitous environment. And it can also extract the place types through the geocoding and the social address domain ontology. The knowledge manager is the core component to provide the personalized services, and consists of activity reasoner, user pattern learner and service recommender to provide the services predict by extracting the optimized service from user situations. Activity reasoner uses the ontology reasoning and user pattern learner learns with previous service usage history and contexts. And to design service recommender easy to flexibly apply in dynamic environments, service recommender recommends service in the only use of current accessible contexts. Finally, we evaluate the learner and recommender of proposed system by simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5344-5356
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• 2018

Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.