• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.1-8
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• 2021

Cloud computing has proven its efficiency, especially after the increasing number of cloud services offered by a wide range of cloud providers, from different domains. Despite, these cloud services are mostly heterogeneous. Consequently, and due to the rising interest of cloud consumers to adhere to a multi-cloud environment instead of being locked-in to one cloud provider, the need for semantically interconnecting different cloud services from different cloud providers is a crucial and important task to ensure. In addition, considerable research efforts proposed interoperability solutions leading to different representation models of cloud services. In this work, we present our solution to overcome this limitation, precisely in the IAAS service model. This solution is a framework permitting the semantic interoperability of different IAAS resources in a multi-cloud environment, in order to assist cloud consumers to retrieve the cloud resource that meets specific requirements.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.65-74
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• 2012

Cloud services are used for improving business. Moreover, customer relationship management(CRM) approaches use social networking as tools to enhance services to customers. However, most cloud systems do not support the semantic structures, and because of this, vital information from social network sites is still hard to process and use for business strategy. This paper proposes a collaboration framework based on social semantic web for cloud system. The proposed framework consists of components to support social semantic web to provide an efficient collaboration system for cloud consumers and service providers. The knowledge acquisition module extracts rules from data gathered by social agents and these rules are used for collaboration and business strategy. This paper showed the implementations of processing of social network site data in the proposed semantic model and pattern extraction which was used for the virtual grouping of cloud service providers for efficient collaboration.

• 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.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.389-397
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• 2017

Recently semantic web document is produced and added in repository in a cloud computing environment and requires an intelligent semantic agent for analytical classification of documents and information retrieval. The traditional methods of information retrieval uses keyword for query and delivers a document list returned by the search. Users carry a heavy workload for examination of contents because a former method of the information retrieval don't provide a lot of semantic similarity information. To solve these problems, we suggest a key word frequency and concept matching based semantic clustering model using hadoop and NoSQL to improve classification accuracy of the similarity. Implementation of our suggested technique in a cloud computing environment offers the ability to classify and discover similar document with improved accuracy of the classification. This suggested model is expected to be use in the semantic web retrieval system construction that can make it more flexible in retrieving proper document.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.71-80
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• 2012

Much research has been recently accomplished for standardization in cloud computing. However, little research on standardization of data sharing and exchanging has been studied. Most of all, the corresponding standardization organization suggests no specific standardization items and reference model. This paper defines the current standardization problems and proposes a set of specific standardization items and a reference model for supporting the semantic interoperability. To achieve the goal of this paper, the overall standardization trend in cloud computing is first analyzed. Especially, this paper describes the status of standard development for addressing the semantic interoperability of data. This paper also defines the potential standardization items based on the concepts of standards in the data exchanging and management field, which are used for developing standards in various fields. Finally, the reference model is describe to show the relationships between items and overall semantic interoperability process. This paper can be used as a guideline for development of standards and also can facilitate standardization of cloud computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.843-871
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• 2015

Cloud computing evolve as a cost effective business model for IT companies to focus on their core business without perturbing on infrastructure related issues. Hence, major IT firms and Small & Medium Enterprises (SME) are adopting cloud services on rental basis from cloud providers. Cloud Service level agreements (SLA) act as a key liaison between consumers and providers on renting Anything as a Service (AaaS). Design of such an agreement must aim for greater profit to providers as well as assured availability of services to consumers. However in reality, cloud SLA is not satisfying the parties involved because of its inherent complex nature and issues. Also currently most of the agreements are unilateral to favour the provider. This study focuses on comprehensive, 360-degree survey on different aspects of the cloud service agreements. We detailed the life cycle of SLA based on negotiation, different types of SLA, current standards, languages & characteristics, metrics and issues involved in it. This study will help the cloud actors to understand and evaluate the agreements and to make firm decision on negotiation. The need for standardized, bilateral, semantic SLA has also been proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.305-306
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• 2013

In this paper, we proposes a document summarization method using the weighting based on cloud. The proposed method can minimize the user intervention to use the relevance feedback. It also can improve the quality of document summaries because the inherent semantic of the sentence set are well reflected by term weighting derived from semantic feature using nonnegative matrix factorizaitno based cloud.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.968-969
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• 2013

In this paper, we proposes a document summarization method using the weighting based on cloud. The proposed method can minimize the user intervention to use the relevance feedback. It also can improve the quality of document summaries because the inherent semantic of the sentence set are well reflected by term weighting derived from semantic feature using nonnegative matrix factorizaitno based cloud.

• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.35-42
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• 2012

Defense Modeling and Simulation aims to provide a computerized war environment where we can analyze weapon systems realistically. As we invest significant efforts to represent weapon systems and their operational environments on the computer, there has been an increasing need to reuse predefined weapon models. In this paper, we introduce OB-Cloud (Ontology-Based Cloud storage) to utilize predefined weapon models. OB-Cloud has been implemented as a repository for OpenSIM (Open Simulation engine for Interoperable Models), which is an integrated simulation environment for aiding weapons effectiveness analysis, under the development of our research team. OB-Cloud uses weapon ontology and thesaurus dictionaries to provide semantic search for reusable models. In this paper, we present repository services of OB-Cloud, including registration of weapon models and semantic retrieval of similar models, and illustrate how we can improve reusability of weapon models, through an example.