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Hybrid cloud computing is considered a viable and cost-effective approach to satisfy the inability of a private cloud to meet the user requirements. The information status of the selected public cloud service may change at runtime which should be reflected at the broker server database. This research illustrates a mechanism to assist the IaaS discovery system to assess the status change of the selected public cloud service and to update the broker server database. The paper aims to discuss these issues.

A prototype was developed with the broker server as the main component in the service discovery system containing the status information of the selected public cloud service. The merge-ontology and patch-update methods were proposed, the processing cost details of each were measured, and the methods were evaluated.

Experimental results showed that in the merge update, the merging process incurred much longer time than its required communication, contributing to long overall time. Relatively, the patch update method incurred much less time than its counterpart.

The proposed mechanism is experimental with some ideal assumptions, and so further work in real conditions is needed for its improvement.

This research is believed to be the first proposal to investigate ontology merge/patch methods to support ontology update in the broker server database of a hybrid cloud and will serve as a reference to researchers in the field.

The purpose of this paper is to analyze cloud reviews according to the end-user context and requirements.

propose a comprehensive knowledge base composed of interconnected Web Ontology Language, namely, modular ontology for cloud service opinion analysis (SOPA). The SOPA knowledge base will be the basis of context-aware cloud service analysis using consumers' reviews. Moreover, the authors provide a framework to evaluate cloud services based on consumers' reviews opinions.

The findings show that there is a positive impact of personalizing the cloud service analysis by considering the reviewers' contexts in the performance of the framework. The authors also proved that the SOPA-based framework outperforms the available cloud review sites in term of precision, recall and F-measure.

Limited information has been provided in the semantic web literature about the relationships between the different domains and the details on how that can be used to evaluate cloud service through consumer reviews and latent opinions. Furthermore, existing approaches are lacking lightweight and modular mechanisms which can be utilized to effectively exploit information existing in social media.

The SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.

The SOPA ontology is capable of representing the content of a product/service as well as its related opinions, which are extracted from the customer's reviews written in a specific context. Furthermore, the SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.

The booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.

To solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.

An organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.

The case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.

To improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.

Cloud computing provides a dynamic, heterogeneous and elastic environment by offering accessible ‘cloud services’ to end-users. The tasks involved in making cloud services available, such as matchmaking, selection and composition, are essential and closely related to each other. Integration of these tasks is critical for optimal composition and performance of the cloud service platform. More efficient solutions could be developed by considering cloud service tasks collectively, but the research and academic community have so far only considered these tasks individually. The purpose of this paper is to propose an integrated QoS-based approach for cloud service matchmaking, selection and composition using the Semantic Web.

In this paper, the authors propose a new approach using the Semantic Web and quality of service (QoS) model to perform cloud service matchmaking, selection and composition, to fulfil the requirements of an end user. In the Semantic Web, the authors develop cloud ontologies to provide semantic descriptions to the service provider and requester, so as to automate the cloud service tasks. This paper considers QoS parameters, such as availability, throughput, response time and cost, for quality assurance and enhanced user satisfaction.

This paper focus on the development of an integrated framework and approach for cloud service life cycle phases, such as discovery, selection and composition using QoS, to enhance user satisfaction and the Semantic Web, to achieve automation. To evaluate performance and usefulness, this paper uses a scenario based on a Healthcare Decision-Making System (HDMS). Results derived through the experiment prove that the proposed prototype performs well for the defined set of cloud-services tasks.

As a novel concept, our proposed integrated framework and approach for cloud service matchmaking, selection and composition based on the Semantic Web and QoS characterisitcs (availability, response time, throughput and cost), as part of the service level agreement (SLA) will help the end user to match, select and filter cloud services and integrate cloud-service providers into a multi-cloud environment.

The main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized, decentralized, and hybrid. Moreover, in this classification, the peer-to-peer (P2P) and agent-based mechanisms are considered the parts of the decentralized mechanism. This paper investigates the main improvements in these three main categories and outlines new challenges. Moreover, the other goals are analyzing the current challenges in a range of problem areas related to cloud discovery mechanisms and summarizing the discussed service discovery techniques.

Systematic literature review (SLR) is utilized to detect, evaluate and combine findings from related investigations. The SLR consists of two key stages in this paper: question formalization and article selection processes. The latter includes three steps: automated search, article selection and analysis of publication. These investigations solved one or more service discovery research issues and performed a general study of an experimental examination on cloud service discovery challenges.

In this paper, a parametric comparison of the discovery methods is suggested. It also demonstrates future directions and research opportunities for cloud service discovery. This survey will help researchers understand the advances made in cloud service discovery directly. Furthermore, the performed evaluations have shown that some criteria such as security, robustness and reliability attained low attention in the previous studies. The results also showed that the number of cloud service discovery–related articles rose significantly in 2020.

This research aimed to be comprehensive, but there were some constraints. The limitations that the authors have faced in this article are divided into three parts. Articles in which service discovery was not the primary purpose and their title did not include the related terms to cloud service discovery were also removed. Also, non-English articles and conference papers have not been reviewed. Besides, the local articles have not been considered.

One of the most critical cloud computing topics is finding appropriate services depending on consumer demand in real-world scenarios. Effective discovery, finding and selection of relevant services are necessary to gain the best efficiency. Practitioners can thus readily understand various perspectives relevant to cloud service discovery mechanisms. This paper's findings will also benefit academicians and provide insights into future study areas in this field. Besides, the drawbacks and benefits of the analyzed mechanisms have been analyzed, which causes the development of more efficient and practical mechanisms for service discovery in cloud environments in the future.

This survey will assist academics and practical professionals directly in their understanding of developments in service discovery mechanisms. It is a unique paper investigating the current and important cloud discovery methods based on a logical categorization to the best of the authors’ knowledge.

The purpose of this study was to compare the ease of use and the effectiveness of several interfaces for retrieving tagged images.

A number of participants were randomly assigned to one of four retrieval interfaces: tag search in a search box; faceted tag search in a search box; selecting terms from the tag cloud of all the tags in the database; and selecting concepts from an ontology created from the tags assigned to the images. Each interface was tested by 21 users.

The results show that the highest recall on average was achieved by users of the ontology interface, for seven out of the ten tasks, however, users were more satisfied with the textbox‐based search than the cloud or the ontology.

The experiment was rather specific, and more studies are needed in order to generalize the findings.

With the widespread use of tagging on the web it is of importance to examine whether tagging enables resource discovery. This study shows that in addition to the tags, the retrieval interface also influences user satisfaction and retrieval success.

Cloud manufacturing (CMfg) is a networked manufacturing mode that promotes the agile, service-oriented, green and intelligent development of the manufacturing industry. Although some scholars have reviewed related studies of CMfg from multiple perspectives, these reviews are not fully systematic or well justified and fail to fully reveal the key characteristics in the development process of CMfg. The purpose of this paper is to systematically review the relevant research on CMfg via identification of key characteristics of definition, architecture, supporting technology and application of CMfg to provide critical information in decision support for the innovation and development of CMfg.

This study systematically reviews the relevant research on CMfg across theoretical methods to technical applications by integrating quantitative and qualitative methods. Word cloud method is used to quantitatively analyse the structure and feature of different definitions of CMfg. The principle of System Science is used to explore the basic components and functions of various CMfg architectures and their common and differing characteristics. A multi-level technology framework is developed to explore the development status of CMfg supporting technologies. A multi-stage application classification is proposed to reveal the application status of CMfg.

Through literature review, this study found that CMfg architecture is currently dominated by general architectures and lacks architectures that fit the actual enterprise characteristics; CMfg supporting technology is mature in the traditional cloud computing-based technology, but it is still weak in the development of virtualization and servitization technology, service scheduling technology; CMfg application is still in the initial stage and still lacks a relatively complete system application. By analysing the development status of CMfg, this study also identified potential research directions of CMfg in information management, service composition and evaluation, system application and sustainable development and other aspects.

This paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. Chinese articles account for more than half of the total. The reason is that CMfg was proposed by the Chinese and CMfg is suitable for the development of China's manufacturing industry because of China's intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg.

Prior literature reviews ignore the identification and analysis of key feature identification for the current development of CMfg, including common and unique feature identification of different CMfg architectures and functions, multi-layer analysis and interpretation of CMfg technology and different stage analysis of CMfg applications. This study addresses these limitations and provides a comprehensive literature review.

The purpose of this paper is to evaluate ontologies with respect to the linked data principles. This paper presents a concrete interpretation of the four linked data principles applied to ontologies, along with an implementation that automatically detects violations of these principles and fixes them (semi-automatically). The implementation is applied to a number of state-of-the-art ontologies.

Based on a precise and detailed interpretation of the linked data principles in the context of ontologies (to become as reusable as possible), the authors propose a set of algorithms to assess ontologies according to the four linked data principles along with means to implement them using a Java/Jena framework. All ontology elements are extracted and examined taking into account particular cases, such as blank nodes and literals. The authors also provide propositions to fix some of the detected anomalies.

The experimental results are consistent with the proven quality of popular ontologies of the linked data cloud because these ontologies obtained good scores from the linked data validator tool.

The proposed approach and its implementation takes into account the assessment of the four linked data principles and propose means to correct the detected anomalies in the assessed data sets, whereas most LD validator tools focus on the evaluation of principle 2 (URI dereferenceability) and principle 3 (RDF validation); additionally, they do not tackle the issue of fixing detected errors.

The purpose of this study is to analyze how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment.

This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low-level computation (fog layer) and cloud computing for processing the data (application layer).

A technical in-depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real-world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach.

Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced artificial intelligence-based algorithms on this information to produce useful knowledge, are described together with the derived social benefits.

The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments.

Ontology and Linked Data (LD) are the two prominent web technologies that have emerged in the recent past. Both of them are at the center of Semantic Web and its applications. Researchers and developers from both academia and business are actively working in these areas. The increasing interest in these technologies promoted the growth of LD sets and ontologies on the web. The purpose of this paper is to investigate the possible relationships between them. The effort is to investigate the possible roles that ontologies may play in further empowering the LD. In a similar fashion, the author also studies the possible roles that LD may play to empower ontologies.

The work is mainly carried out by exploring the ontology- and LD-based real-world systems, and by reviewing the existing literature.

The current work reveals, in general, that both the technologies are interdependent and have lots to offer to each other for their faster growth and meaningful development. Specifically, anything that we can do with LD, we can do more by adding an ontology to it.

The author envisions that the current work, in the one hand, will help in boosting the successful implementation and the delivery of semantic applications; on the other hand, it will also become a food for the future researchers in further investigating the relationships between the ontologies and LD.

So far, as per the author’s knowledge, there are very little works that have attempted in exploring the relationships between the ontologies and LD. In this work, the author illustrates the real-world systems that are based on ontology and LD, discusses the issues and challenges and finally illustrates their interdependency discussing some of the ongoing research works.