• Journal of Digital Contents Society
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• v.19 no.2
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• pp.335-341
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• 2018

There exists a limit in efficient processing of mixed workloads that database markets requires in recent years since existing database systems utilize a static page storage model. In this paper we propose a dynamic page storage model that can reflect the characteristics of mixed workloads. We also describe how to extract optimized column groups from given mixed workloads and how to construct pages dynamically. Finally, we show in our experiments that the proposed model is more efficient than the existing model in processing given mixed workloads.

• Journal of Environmental Policy
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• v.9 no.1
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• pp.31-55
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• 2010

This study aims to estimate potential climate change damage in Korea using the PAGE model. This study reviewed previous a reasearch to compare relative sensitivity to climate change in Korea and other regions to generate climate change damage function. It was found that sensitivity to climate change in Korea is similar to other Organization for Economic Cooperation and Development (OECD) countries. This study estimated climate change impact for three scenarios. If no action is taken, climate change damage cost in Korea could reach US$ 12,928 ~ 57,900 M. Cumulative Net Present Value (NPV)of climate change impact from 1990 to 2100 would be between US$ 143,226 ~ 921,701 Mdepending on emission scenarios. However, this result should be interpreted with caution as it draws its damage function based on only a few available references. Results also showed that an adaptation policy could decrease the degree of climate change impact significantly. If an adaptation policy is implemented, climate change impact will be decreased by US$ 11,355 million dollars in Korea in 2100.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.93-98
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• 2022

Recently, fast storage media such as Optane have emerged, and memory system configurations designed for disk storage should be reconsidered. In this paper, we analyze the effect of the page size on the memory system performances when fast storage is adopted. Based on this, we design a page size model that can guide an appropriate page size for given workloads in virtualized environments. Configuring different page sizes for various workloads is not an easy matter in traditional systems, but due to the widespread adoption of cloud systems, page sizing performed in our model is feasible for virtual machines, which are generated for executing specific workloads. Simulation experiments under various virtual machine scenarios show that the proposed model improves the memory access time significantly by configuring page sizes for given workloads.

• The KIPS Transactions:PartD
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• v.17D no.5
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• pp.359-370
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• 2010

As web 2.0 becomes one of the important architecture styles, more web applications adopt single page structure instead of multiple web pages and navigations between pages. A single page web application client, called a mashup client in this paper, interfaces more than one services and allows users to navigate in the page. A mashup client page includes complicated functions and has to handle various styles of services and user requirements, and therefore is usually developed manually. In this paper, we propose a model driven code generation approach for in-page navigations. We propose a page model and view navigation design approach, applying REST service architecture patterns. Then, we consider type conditions for each view to have service calls or navigation controls. Also, we developed an XForms page code generation system to demonstrate the efficiency of the proposed method. The developed system generates mashup client pages including navigation controls between services and views. This system can generate ready to use codes from service specifications, so this can help to reduce the development overhead. Moreover, our approach is based on formal model and navigation patterns so the generated result code is simple and easy to understand, and includes only the necessary controls. Therefore, the proposed approach can be more effective for the case of a large number of services.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.9-15
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• 2023

Single-Page Application(SPA) requires data transformation for communication with RESTful API. The Backend for Frontend(BFF) pattern handles this transformation in the server, but there is some problem that increases the number of communication and makes development and distribution difficult. In this study, we propose an architecture that maps the ViewModel of SPA and the model of RESTful API directly in SPA. The proposed architecture automatically generates a mapping model between the RESTful API model and the ViewModel using the OpenAPI specification, which is the document model of the RESTful API. The data transfer component of SPA automatically converts RESTful API data and ViewModel using the created model. As a result of comparison with the existing BFF method through case study, the proposed architecture showed higher development productivity than BFF, and as a result of load tests, it recorded about 6% lower server CPU occupancy compared to BFF.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.517-524
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• 2024

This study aims to train and implement a deep learning model for the fusion of website creation and artificial intelligence, in the era known as the AI revolution following the launch of the ChatGPT service. The deep learning model was trained using 3,000 collected web page images, processed based on a system of component and layout classification. This process was divided into three stages. First, prior research on AI models was reviewed to select the most appropriate algorithm for the model we intended to implement. Second, suitable web page and paragraph images were collected, categorized, and processed. Third, the deep learning model was trained, and a serving interface was integrated to verify the actual outcomes of the model. This implemented model will be used to detect multiple paragraphs on a web page, analyzing the number of lines, elements, and features in each paragraph, and deriving meaningful data based on the classification system. This process is expected to evolve, enabling more precise analysis of web pages. Furthermore, it is anticipated that the development of precise analysis techniques will lay the groundwork for research into AI's capability to automatically generate perfect web pages.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1193-1207
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• 2003

Various web applications are developed as the Internet is popularized in many fields. However, in most cases of web application development, systematic analysis is omitted and developers jump into the implementation. Therefore developers have difficulties with applying the development methods for a large scale project. The approach of creating an analysis models of a web application from a business model is proposed for the rapid and efficient development. The analysis process, tasks and techniques are proposed for this approach. The use case diagram and web page list are created from business modes that is depicted using the notation of UML activity diagram. The page diagram and logical / physical database models are created using the use case diagram and the web page list. These analysis models are refined during the detailed design phase. The efficiency of proposed method has been shown using a practical case study which reflects the development project of the web application for supporting the association of auto repair shops.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.1-11
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• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.65-70
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• 2022

As fast storage has become popular, the memory management system designed for hard disks needs to be reconsidered. In this paper, we observe that memory access latency is sensitive to the page size when fast storage is adopted. We find the reason from the TLB miss rate, which has the increased impact on the memory access latency in comparison with the page fault rate, and there is trade-off between the TLB miss rate and the page fault rate as the page size is varied. To handle such situations, we model the page fault rate and the TLB miss rate accurately as a function of the page size. Specifically, we show that the power fit and the exponential fit with two terms are appropriate for fitting the TLB miss rate and the page fault rate, respectively. We validate the effectiveness of our model by comparing the estimated values from the model and real values.