• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.5-14
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• 2017

Many web services which use OAuth Protocol offer users to log in using their personal profile information given by resource servers. This method reduces the inconvenience of the users to register for new membership. However, at the time a user finishes using OAuth client web service, even if he logs out of the client web service, the resource server remained in the login state may cause the problem of leaking personal information. In this paper, we propose a solution to mitigate the threat by providing an additional security behavior check: when a user requests to log out of the Web Client service, he or she can make decision whether or not to log out of the resource server via confirmation notification regarding the state of the resource server. By utilizing the proposed method, users who log in through the OAuth Protocol in the public PC environment like department stores, libraries, printing companies, etc. can prevent the leakage of personal information issues that may arise from forgetting to check the other OAuth related services. To verify our study, we implement a Client Web Service that uses OAuth 2.0 protocol and integrate it with our security behavior check. The result shows that with this additional function, users will have a better security when dealing with resource authorization in OAuth 2.0 implementation.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.847-856
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• 2006

In this paper, we suggested a mobile collaboration framework for supporting mobile services among mobile devices, and designed and implemented on this environment. The suggested framework has three elements; groups of sensors and mobile devices(Fixed and Moving-typed PDAs) and a home server. We designed interfaces for interactions with each other in collaboration environment with three elements described above. The information collected by sensors can be share and exchanged by mobile devices or a home server in accordance with Push and Pull methods. This framework is based on the distributed object group framework(DOGF) we implemented before. Therefore the DOGF provides functions of object group management, storing information and security services to our mobile collaboration framework via application interfaces defined. The information collected by sensors is arranged according to user's security 'demands. And user profile information is used for checking authority of each service object. Each component for executing functions of mobile devices and a home server is implemented by TMO scheme. And we used the TMOSM for interactions between distributed components. Finally, we showed via GUI the executablity of a given healthcare application scenario on our mobile collaboration framework.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.191-205
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• 2005

This paper presents a novel product recommender system as a tool fur differentiated marketing service of online shopping malls. Ihe proposed model uses genetic algorithnt one of popular global optimization techniques, to construct a personalized product recommender systen The genetic algorinun may be useful to recommendation engine in product recommender system because it produces optimal or near-optimal recommendation rules using the customer profile and transaction data. In this study, we develop a prototype of WeLbased personalized product recommender system using the recommendation rules fi:om the genetic algorithnL In addition, this study evaluates usefulness of the proposed model through the test fur user satisfaction in real world.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.77-84
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• 2017

In order to provide healthcare services based on context-awareness techniques in IoT environments, a system requires to collect user profile and environmental information. To collect environmental information, accessing sensor device and gathering sensor data should be proceeded. Although this process is necessary to build environmental information, there is no proper sensor data acquisition model. In this paper, we propose sensor data acquisition model that contains schema to connect each device and to collect various kinds of data from sensor device at one point. In experiment, we demonstrate sensor data acquisition procedures with a description following suggested scheme.

• Journal of The Korean Association of Information Education
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• v.15 no.2
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• pp.219-226
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• 2011

Web personalization has focused on extracting web pages interesting to users, to help users searching wanted information efficiently on the web. One of the main methods to achieve this is by using queries, links and users' preferred words in the pages. In this study, we surveyed from the web users the features of pages that are considered important to themselves in selecting web pages. The survey results showed that the content of the pages is the most important. However, images and readability of the page are rated as high as the content for some users. Based on this result, we present a method for maintaining relative weights of major page features differently in the profile for each user, which is used for personalizing web search results. Performance of the proposed personalization method is analyzed to prove its superiority such that it yields as much as 1.5 times higher rate than the system utilizing both queries and preferred words and about 2.3 times higher rate than a generic search engine.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.197-206
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• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of KIISE:Information Networking
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• v.29 no.1
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• pp.1-14
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• 2002

The most important issue in providing multimedia traffic on a mobile computing environments is to guarantee the mobile host(client) with consistent QoS(Quality of Service). However, the QoS negotiated between the client and network in one cell may not be honored due to client mobility, causing hand-offs between cells. In this paper, a call admission control mechanism is proposed to provide consistent QoS guarantees for multimedia traffics in a mobile computing environment. Each cell can reserve fractional bandwidths for hand-off calls to its adjacent cells. It is important to determine the right amount of reserved bandwidth for hand-off calls because the blocking probability of new calls may increase if the amount of reserved bandwidth is more than necessary. An adaptive bandwidth reservation based on an MPP(Mobility Pattern Profile) and a 2-tier cell structure has been proposed to determine the amount of bandwidth to be reserved in the cell and to control dynamically its amount based on its network condition. We also propose a call admission control based on this bandwidth reservation and "next-cell prediction" scheme using an MPP. In order to evaluate the performance of our call admission control mechanism, we measure the metrics such as the blocking probability of our call admission control mechanism, we measure the metrics such as the blocking probability of new calls, dropping probability of hand-off calls, and bandwidth utilization. The simulation results show that the performance of our mechanism is superior to that of the existing mechanisms such as NR-CAT1, FR-CAT1, and AR-CAT1.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.916-920
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• 2008

The personal workspace consists of user- specified computing environment such as user profile, applications and their configurations, and user data. Mobile computing devices (i.e., cellular phones, PDAs, laptop computers, and Ultra Mobile PC) are getting smaller and lighter to provide personal work-space ubiquitously. However, various personal work-space mobility solutions (c.f. VMWare Pocket ACE[1], Mojopac[2], u-PC[3], etc.) are appeared with the advance of virtualization technology and portable storage technology. The personal workspace can be loaded at public PC using above solutions. Especially, we proposed a framework called ubiquitous personal computing environment (u-PC) that supports mobility of personal workspace based on wireless iSCSI network storage in our previous work. However, previous u-PC could support limited applications, because it uses IRP (I/O Request Packet) forwarding technique at filter driver level on Windows operating system. In this paper, we implement OS-level virtualization technology using system call hooking on Windows operating system. It supports personal workspace mobility and covers previous u-PC limitation. Also, it overcomes personal workspace loading overhead that is limitation of other solutions (i.e., VMWare Pocket ACE, Mojopac, etc). We implement a prototype consisting of Windows XP-based host PC and Linux-based mobile device connected via WiNET protocol of UWB. We leverage several use~case models of our framework for proving its usability.

• Journal of Korean Library and Information Science Society
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• v.39 no.4
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• pp.353-376
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• 2008

The main purpose of this research is to develop a methodology for designing metadata application profiles(AP) and to design a comprehensive AP for Korea Art and Education Service(KACES), The process of developing a methodology is divided into three steps, analysis of standard metadata schema, analysis of user needs, and content analyses. Metadata principles and guidelines were utilized in designing a KACES AP along with the developed methodology in this study. This paper describes what role the methodology developed here plays in devising metadata elements. The paper also discusses the benefits of three methods for AP design. The study has values in that it provides implications for designing APs for other domains.