• 한국IT서비스학회지
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• 제14권2호
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• pp.253-273
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• 2015

Cloud computing is provided on demand service via the internet, allowing users to pay for the service they actually use. Categorized as one kind of cloud computing, SaaS is computing resource and software sharing model with can be accessed via the internet. Based on virtualization technology, SaaS is expected to improve the efficiency and quality of the IT service level and performance in company. Therefore this research limited cloud services to SaaS especially focused on collaborative application service, and attempts to identify the factors which impact the performance of collaboration and intention to use. This study adopts technological factors of cloud SaaS services and factors of task characteristics to explore the determinants of collaborative performance and intention to use. An experimental study using student subjects with Google Apps provided empirical validation for our proposed model. Based on 337 data collected from respondents, the major findings are following. First, the characteristics of cloud computing services such as collaboration support, service reliability, and ease of use have positive effects on perceived usefulness of collaborative application while accessability, service reliability, and ease to use have positive effects on intention to use. Second, task interdependence has a positive effects on collaborative performance while task ambiguity factor has not. Third, perceived usefulness of collaborative application have positive effects on intention to use.

• 디지털산업정보학회논문지
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• 제9권1호
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• pp.43-52
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• 2013

Pervasive computing and social network are good resources in recommendation method. Collaborative filtering is one of the most popular recommendation methods, but it has some limitations such as rating sparsity. Moreover, it does not consider social network in pervasive computing environment. We propose an effective proactive friend recommendation method using social network and contexts in pervasive computing environment. In collaborative filtering method, users need to rate sufficient number of items. However, many users don't rate items sufficiently, because the rating information must be manually input into system. We solve the rating sparsity problem in the collaboration filtering method by using contexts. Our method considers both a static and a dynamic friendship using contexts and social network. It makes more effective recommendation. This paper describes a new friend recommendation method and then presents a music friend scenario. Our work will help e-commerce recommendation system using collaborative filtering and friend recommendation applications in social network services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권3호
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• pp.411-427
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• 2010

Data sharing is an essential process for collaborative works particularly in the banking, finance and healthcare industries. These industries require many collaborative works with their internal and external parties such as branches, clients, and service providers. When data are shared among collaborators, security and privacy concerns becoming crucial issues and cannot be avoided. Privacy is an important issue that is frequently discussed during the development of collaborative systems. It is closely related with the security issues because each of them can affect the other. The tradeoff between privacy and security is an interesting topic that we are going to address in this paper. In view of the practical problems in the existing approaches, we propose a collaborative framework which can be used to facilitate concurrent operations, single point failure problem, and overcome constraints for two-party computation. Two secure computation protocols will be discussed to demonstrate our collaborative framework.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2005년도 춘계 학술대회논문집
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• pp.35-40
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• 2005

Up to recently, with the improvement of a computer power and high speed of network technology, advanced countries have researched a construction of the e-Science environment. As a major application part, a construction for environment of CFD, also, have studied together. During the research, people realize that not sharing hardware but also appropriate software development is really important to realize the environment. This paper describes about a construction of a collaborative research environment in the KISTI: Clients can connect to the computing resources through the web portal, run the Cactus simulation.: According to the computing resources, the simulation can migrate to some site to find better computing power.: Result of the calculation visualize at the web portal directly so that researchers of remote site can be share and analyze the result collaborative ways.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1456-1463
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• 2017

In recent times, IaaS (Infrastructure as a Services) have been rapidly evolving to allow developers to easily and efficiently access work in the server and network areas for development of a web of App based on cloud computing. In this study, we developed the collaborative image editing tool App based on Cloud-computing, by adopting AWS of representative company that develops IaaS. First, it is crucial to understand various situation conditions for representative infrastructure services: AWS, Azure and Google (GCP). This may have the effect of reducing manpower and development time, but as each company has different policy and technical support, we need a new study every time the environment changes of infrastructure services. We tried to develop a hybrid-App so that users with various devices can collaborate work each other by utilizing the infrastructure service AWS through the process of developing the image editing authoring tool based on the cloud computing. The future studies should continue about compatibility issues and support issues in order to minimize the problems of overseas infrastructure services, but we think that domestic cloud computing policies and developments should be urgently considered.

• 한국인터넷방송통신학회논문지
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• 제14권1호
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• pp.101-107
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• 2014

본 논문에서는 협업 필터링 알고리즘을 클러스터링 기반으로 분산 환경에서 구현하여, 추천을 위한 수행 시간을 최적화 하는 방법에 대한 제안을 한다. 하둡 기반으로 시스템을 구성하였고, 분산 Min-hash 클러스터링 기반의 협업 필터링 방법을 제안하고, 이를 기반으로 분산 추천 시스템을 구성하였다. 분산 사용자 기반 협업 필터링 기법을 사용하여 무비렌즈 (Movie Lens)의 영화 평점 데이터를 기반으로 각각의 사용자에게 알맞은 영화를 추천해주는 분산추천 시스템을 구현하고 실험을 통하여 성능의 우수성을 검증하였다.

• 한국경영과학회지
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• 제20권3호
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• pp.1-16
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• 1995

Group collaborative systems are recently emerging to support a group of users engaged in common tasks such as group decision making, engineering design, group scheduling, or collaborative writing. This paper provides an change management framework for the group collaborative system to facilitate managing dependency relationship between shared objects and dependent user views, and coordinating change and propagation activities between the two in distributed computing environments. Specifically, the framework adopts an object-oriented database paradigm and presents several object constructs capturing dependency management and change notification mechanisms. First, it introduces change management mechanisms with transient shared objects and secondly, it extends them into presistent object computing environment by integrating transaction management mechanisms and change notification mechanisms. A prototype change management framework is developed on a commercial object-oriented database management system.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.491-494
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• 1998

This paper describes the design and implementation of file system which allows the collaborative computing in mobile environment. The design goal is to make a logically one file system in the distributed computer systems. The characteristics of frequent, foreseeable and variable disconnections in a mobile environment wrer taken into consideration. We introduce an auto-hoarding system that provides the availability of large number of nodes which are weakly and intermittently connected. The data consistency problems in distributed or replicated mobile data are also discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권2호
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.