• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.39-49
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• 2019

Advances in IoT data analysis technology have made it easier to analyze situation and provide interactive services based on the context. Most of digital signage application have been used to provide information uni-directionally, but in the future it will evolve to provide personalized content according to the individual user situation and responses. However, it is not easy to modify or apply the existing interactive digital signage platforms due to their hardware dependency. The proposed platform is modularized by dividing main functions into two, the cloud and the edge, so that advertisement resources can be easily generated and registered. Thus, interactive advertisement can be rendered in a timely manner based on sensor analysis results. At the edge, personal data can be processed to minimize privacy issues, and real-time IoT sensor data can be analyzed for quick response to the signage player. The cloud is easier to access and manage by multiple users than edge. Therefore, the signage content generation module improves accessibility and flexibility by handling advertisement contents in the cloud so that multiple users can work together on the cloud platform. The proposed platform was developed and simulated in two aspects. First is the provider who provides the signage service, and second is the viewer who uses the content of the signage. Simulation results show that the proposed platform enables providers to quickly construct interactive signage contents and responses appropriately to the context changes in real-time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.716-722
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• 2016

In the recent rapidly changing IT environment, the amount of smart digital data is growing exponentially. As a result, in many areas, utilizing big data research and development services and related technologies is becoming more popular. In SMART learning, big data is used by students, teachers, parents, etc., from a perspective of the potential for many. In this paper, we describe big data and can utilize it to identify scenarios. Big data, obtained through customized learning services that can take advantage of the scheme, is proposed. To analyze educational big data processing technology for this purpose, we designed a system for big data processing. Education services offer the measures necessary to take advantage of educational big data. These measures were implemented on a test platform that operates in a cloud-based operations section for a pilot training program that can be applied properly. Teachers try using it directly, and in the interest of business and education, a survey was conducted based on enjoyment, the tools, and users' feelings (e.g., tense, worried, confident). We analyzed the results to lay the groundwork for educational use of big data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2707-2712
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• 2012

Desktop-based computing environment is changed into mobile computing using smart phone and cloud computing providing common behavior and big data by network. Because of this transformation software development and operating environment is changed into heterogeneous distributed environment. As a result, dynamic service composition or changement is required. However, there is few research of techniques supporting service composition or changement dynamically in this situation. This paper suggests a technique for customizing services dynamically of mobile applications based on android platform. Especially we propose a customization technique of service by applying ontology technique to improve sharing and reuse of service. We applied proposed technique into meeting notification system, and obtain that it can be customized into various services such as email, sms, twitter service, and so on.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.2
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• pp.73-91
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• 2016

This study sheds light on the quality aspect of cloud computing services as next IT platform. Three tasks of the research are to extract the quality factors of cloud service from the user's viewpoint, empirically analyze the perceptual differences between the user group and the provider group by applying the IPA technique, and suggest some quality factors that need to be improved. Based on the previous researches and focus group evaluation, 13 quality factors have been established. Two field surveys have been performed respectively to collect the perceptual importance and satisfaction level of the users and the providers. It is shown that the quality satisfaction of the user group is lower than the quality perceived by the providers. And there exist significant differences between two groups in respect to quality importance level and IPA matrix. In conclusion, 6 quality factors that need to be improved are suggested such as service functionality, service availability, interoperability, scalability, confidentiality, and provider's responsiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5231-5248
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• 2016

As a service-oriented paradigm, web service composition has obtained great attention from both academia and industry, especially in the area of cloud service. Nowadays more and more web services providing the same function but different in QoS are available in cloud, so an important mission of service composition strategy is to select the optimal composition solution according to QoS. Furthermore, the selected composition solution should satisfy the service level agreement (SLA) which defines users' request for the performance of composite service, such as price and response time. A composite service is feasible only if its QoS satisfies user's request. In order to obtain composite service with the optimal QoS and avoid SLA violations simultaneously, in this paper we first propose a QoS evaluation method which takes the SLA satisfaction into account. Then we design a service selection algorithm based on our QoS evaluation method. At last, we put forward a parallel running strategy for the proposed selection algorithm. The simulation results show that our approach outperforms existing approaches in terms of solutions' optimality and feasibility. Through our running strategy, the computation time can be reduced to a large extent.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1290-1300
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• 2016

Recently, various middle box services have been developed and applied to provide network functions to end nodes of the network. Especially, network virtualization is increasingly proceeding by applying the virtualization technologies of cloud computing field to network field, and network platforms for various flexible services are being developed to connect among the virtual network devices. Carrier-grade Network Address Translation (CGNAT) is also one of these flexible network services. This paper designs and implements the DPDK-based CGNAT framework that provides flexibility and maximizes address translation throughput. Our framework achieves 15.5 times higher throughput than the address translation service by Linux kernel.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.33-39
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• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.103-110
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• 2020

The EDISON is a platform that supports numerical analysis for problem solving in computational science and engineering. We provide a cloud service for users, and provide an environment to access and execution of the simulation service on the web. For now, the EDISON platform provides simulation services for eight applied field on computational science engineering. Users can check the numerical analysis result by web in the computational science and engineering platform. In addition, various services such as community activity with other researchers, and the configuration of simulation environment by user 's needs can be provided. A representative service of the EDISON platform is a web-based simulation service that performs numerical analysis for problem solving of various computational science and engineering. Currently, EDISON platform provides workbench simulation service. It is the web-based simulation execution environment, and result analysis to provide simulation regardless of various personal computing resource or environment in each numerical analysis. In this paper, we build an interface for pre and post processor that can be used in conjunction with the workbench-based simulation service provided by EDISON platform. We provide a development environment with interface that is implemented by applying a pre and post processor optimized for the simulation service. According to simulation and execution are performed by linking the new workbench-based simulation service to the pre and post processor.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.1-13
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• 2013

Recent advance and popularization of smart devices and web application services based on cloud computing have made end-users to directly produce and, at the same time, consume the image contents. This leads to demands of unified contents management services. Thus, this paper proposestag clustering method based on semantic similarity for effective image categorization. We calculate the cost of semantic similarity between tags and cluster tags that are closely related. If tags are in a cluster, we suppose that images with them are also in a same cluster. Furthermore, we could recommend tags for new images on the basis of initial clusters.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.692-703
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• 2021

Recently, with the development of computer graphics technology, research on technology for expressing real objects as more realistic virtual graphics is being actively conducted. Point cloud is a technology that uses numerous points, including 2D spatial coordinates and color information, to represent 3D objects, and they require huge data storage and high-performance computing devices to provide various services. Video-based Point Cloud Compression (V-PCC) technology is currently being studied by the international standard organization MPEG, which is a projection based method that projects point cloud into 2D plane, and then compresses them using 2D video codecs. V-PCC technology compresses point cloud objects using 2D images such as Occupancy map, Geometry image, Attribute image, and other auxiliary information that includes the relationship between 2D plane and 3D space. When increasing the density of point cloud or expanding an object, 3D calculation is generally used, but there are limitations in that the calculation method is complicated, requires a lot of time, and it is difficult to determine the correct location of a new point. This paper proposes a method to generate additional points at more accurate locations with less computation by applying 2D interpolation to the image on which the point cloud is projected, in the V-PCC technology.