• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.161-168
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• 2015

It is difficult to provide proper cloud services for cloud users, because the number of cloud services are increasing and the type of cloud services are diversifying. To overcome this problem, the concept of cloud service broker is presented to mediate cloud services between cloud providers and tenant. The most important role of cloud service broker is to finding cloud services that fulfill requirements of tenant. However, current existing cloud service broker conduct passive requirements analysis process with cloud service expert's assistance. In addition, the systematic functional and non-functional requirement analysis is insufficient. Therefore, we need the new methods for requirement analysis to find nearest service that matches with requirement of tenant. In this paper, we apply pairwise comparison from AHP method to analyze requirement automatically and systematically. It calculates score of service by comparing requirement with service specification, calculating importance rate, and so on.

• Journal of Information Technology Applications and Management
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• v.26 no.5
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• pp.57-65
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• 2019

Recommendation Systems are information technologies that E-commerce merchants have adopted so that online shoppers can receive suggestions on items that might be interesting or complementing to their purchased items. These systems stipulate valuable assistance to the user's purchasing decisions, and provide quality of push service. Traditionally, Recommendation Systems have been designed using a centralized system, but information service is growing vast with a rapid and strong scalability. The next generation of information technology such as Cloud Computing and Big Data Environment has handled massive data and is able to support enormous processing power. Nevertheless, analytic technologies are lacking the different capabilities when processing big data. Accordingly, we are trying to design a conceptual service model with a proposed new algorithm and user adaptation on dynamic recommendation service for big data environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1820-1840
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• 2017

Aiming at the phenomenon of false information issued by service providers in cloud computing environment, a method for service evaluation based on fuzzy theory is put forward in this paper. According to the quality of services provided by cloud service providers and their behavior during interactions, a trust relationship between cloud service providers and cloud service consumers is established, which can be quantified by using fuzzy theory. The quality of services is evaluated by drawing on the trust relationship. In our method, the recommendation credibility of a cloud service consumer is determined through behavior similarity with evaluators and a praise factor. The introduction of the praise factor better suits the phenomenon of a high-quality service getting more repeat customers. The negative impact of dishonest customers is reduced, and the accuracy of trust and cloud service quality evaluation is improved by introducing a confidence factor that can be dynamically adjusted. The experimental results show that our method can effectively and accurately evaluate the trust value and service quality of providers, while weakening the influence of dishonest consumers, and quickly detect dishonest service providers. This is beneficial for consumers trying to find high quality service providers for similar services.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.257-264
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• 2012

The development of mobile devices and the spread of wireless network help share and exchange information and resources more easily. The bond them to Cloud Computing technology help pay attention to "Mobile Cloud" service, so there have been being a lot of studies on "Mobile Cloud" service. Especially, the important of 'Recommendation Service' which is customized for each user's preference and context has been increasing. In order to provide appropriate recommendation services, it enables to recognize user's current state, analyze the user's profile like user's tendency and preference, and draw the service answering the user's request. Most existing frameworks, however, are not very suitable for mobile devices because they were proposed on the web-based. And other context information except location information among user's context information are not much considered. Therefore, this paper proposed the context-aware framework, which provides more suitable services by using user's context and profile.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2743-2762
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• 2014

Promoted by cloud technology and new websites, plenty and variety of Web services are emerging in the Internet. Meanwhile some Web services become outdated even obsolete due to new versions, and a normal phenomenon is that some services work well only with other services of older versions. These laggard or improper services are lowering the performance of the composite service they involved in. In addition, using current technology to identify proper semantic services for a composite service is time-consuming and inaccurate. Thus, we proposed a clustering method and a recommendation method to deal with these problems. Clustering technology is used to classify semantic services according to their topics, functionality and other aspects from plenty of services. Recommendation technology is used to predict the possible preference of a composite service, and recommend possible component services to the composite service according to the history information of invocations and similar composite services. The experiments show that our clustering method with the help of Ontology and TF/IDF technology is more accurate than others, and our recommendation method has less average error than others in the series of missing rate.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.283-288
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• 2015

Scientists gain benefits from on-demand scalable resource provisioning, and various computing environments by using cloud computing resources for their applications. However, many cloud computing service providers offer their cloud resources according to their own policies. The descriptions of resource specification are diverse among vendors. Subsequently, it becomes difficult to find suitable cloud resources according to the characteristics of an application. Due to limited understanding of resource availability, scientists tend to choose resources used in previous experiments or over-performed resources without considering the characteristics of their applications. The need for standardized notations on diverse cloud resources without the constraints of complicated specification given by providers leads to active studies on intercloud to support interoperability in hybrid cloud environments. However, projects related to intercloud studies are limited as they are short of expertise in application characteristics. We define an intercloud resource classification and propose semantic resource recommendation based on statistical analysis to provide semantic cloud resource services for an application in hybrid cloud computing environments. The scheme proves benefits on resource availability and cost-efficiency with choosing semantically similar cloud resources using cluster analysis while considering application characteristics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1446-1465
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• 2022

Diversified SaaS applications allow users more choices to use, according to their own preferences. However, the diversification of SaaS applications also makes it impossible for users to choose the best one. Furthermore, users can't take advantage of the functionality between SaaS applications. In this paper, we propose a platform that provides an SaaS mashup service, by extracting interoperable service functions from SaaS-based applications that independent vendors deploy and supporting a customized service recommendation function through log data binding in the cloud environment. The proposed SaaS mashup service platform consists of a SaaS aggregation framework and a log data binding framework. Each framework was concreted by using Apache Kafka and rule matrix-based recommendation techniques. We present the theoretical basis of implementing the high-performance message-processing function using Kafka. The SaaS mashup service platform, which provides a new type of mashup service by linking SaaS functions based on the above technology described, allows users to combine the required service functions freely and access the results of a rich service-utilization experience, using the SaaS mashup function. The platform developed through SaaS mashup service technology research will enable various flexible SaaS services, expected to contribute to the development of the smart-contents industry and the open market.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.121-129
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• 2010

In recent years, Cloud computing is gaining much popularity as it can efficiently utilize the computing resources and hence can contribute to the issue of green IT. So to make the Cloud services commercialized, Cloud markets are necessary and are being developed. As the increasing numbers of various Cloud services are rapidly evolving in the Cloud market, how to select the best and optimal services will be a great challenge. In this paper we present a Cloud service selection framework in the Cloud market that uses a recommender system (RS) which helps a user to select the best services from different Cloud providers (CP) that matches his/her requirements. The RS recommends a service based on the QoS and Virtual Machine (VM) factors of difference CPs. The experimental results show that our Cloud service recommender system (CSRS) can effectively recommend a good combination of Cloud services to consumers.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.893-901
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• 2023

The International Association Marine Aids to Navigation and Lighthouse Authorities (IALA) proposed guidelines for VTS manual operation in 2016 for safe and efficient operation of ship. The Korea Coast Guard (KCG) established and operated 19 VTS centers in ports and coastal waters across the country by 2022 based on the IALA VTS manual and VTS operator's education and training guidelines. In addition, IALA proposed the Inter-VTS Exchange Format (IVEF) Service recommendation (V-145), a standard for data exchange between VTS, in 2011 for efficient e-Navigation system services and safe and efficient VTS service support by VTS authorities. The IVEF service in a common framework for ship information exchange, and it presents seven basic IVEF service (BISs) models. VTS service providers can provide safer and more efficient VTS services by sharing VTS information on joint area using IVEF standards. Based on the BIS data, interaction, and interfacing models, this paper introduced the development of the cloud-based VTS integration services performed by the KCG and the results of the VTS integration platform test-bed for IVEF service implementation. In addition, the results of establishing a cloud VTS integrated platform test-bed for the implementation of IVEF service and implementing the main functions of IVEF service were presented.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.309-315
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• 2012

The dissemination of smartphones is being spread and supplementary services using smartphones are increasing and various as the Mobile network and device are developing rapidly, so smartphones that enables to provide a wide range of services is expected to receive the most attention. It makes users listen to music anytime, anywhere in real-time, use useful applications, and access to Internet to search for information. The service environment is changing on PC into Mobile due to the change of the circumstance mentioned above. these services are done by using just location information rather than other context, and users have to search services and use them. It is essential to have Context-aware technology for personalization recommendation services and the appropriate representation and definition of Context information for context-aware. Ontology is possible to represent knowledge freely and knowledge can be extended by inferring. In addition, design of the ontology model is needed according to the purposes of utilization. This paper used context-aware technologies to implement a user personalization recommendation service. It also defined the context through OWL modeling for user personalization recommendation service and used inference rules and inference engine for context reasoning.