An agility-oriented and fuzziness-embedded semantic model for collaborative cloud service search, retrieval and recommendation

Fang, Daren; Liu, Xiaodong; Romdhani, Imed; Jamshidi, Pooyan; Pahl, Claus

doi:10.1016/j.future.2015.09.025

Cited by 28 publications

(7 citation statements)

References 31 publications

(62 reference statements)

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“…In addition, semantic technologies are used to provide comprehensive service specification across various abstraction levels and service categories. For instance, Fang et al (2016) proposed a fuzziness-embedded and agility-oriented semantic model that captures cloud interactions and details across different abstraction levels including SaaS, PaaS and IaaS. The model can be used to reveal multiple agile interactions among the resources and services within a cloud computing environment.…”

Section: Cloud Interoperability and Portabilitymentioning

confidence: 99%

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Leveraging cloud computing for the semantic web: review and trends

et al. 2019

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Semantic and Cloud Computing technologies have become vital elements for developing and deploying solutions across diverse fields in computing. While they are independent of each other, they can be integrated in diverse ways for developing solutions and this has been significantly explored in recent times. With the migration of web-based data and applications to cloud platforms and the evolution of the web itself from a social, web 2.0 to a semantic, web 3.0 comes the convergence of both technologies. While several concepts and implementations have been provided regarding interactions between the two technologies from existing research, without an explicit classification of the modes of interaction, it can be quite challenging to articulate the interaction modes, hence building upon them can be a very daunting task. Hence, this research identifies and describes the modes of interaction between them. Furthermore, a "cloud-driven" interaction mode which focuses on fully maximising cloud computing characteristics and benefits for driving the semantic web is described; providing an approach for evolving the semantic web and delivering automated semantic annotation on a large-scale to web applications.

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Section: Cloud Interoperability and Portabilitymentioning

confidence: 99%

“…The model can be used to reveal multiple agile interactions among the resources and services within a cloud computing environment. Figure 2 presents the agility-oriented ontology design of the work by Fang et al (2016).…”

Section: Cloud Interoperability and Portabilitymentioning

confidence: 99%

Leveraging cloud computing for the semantic web: review and trends

et al. 2019

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“…Our platform architecture requires some background in architecture, deployment and self-adaptivity [13,30,35,53].…”

Section: Platform and Architecture Conceptsmentioning

confidence: 99%

Fuzzy Container Orchestration for Self-adaptive Edge Architectures

Gand

Fronza

Ioini

et al. 2021

Communications in Computer and Information Science

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The edge or edge computing refers to the computational infrastructure between sensors and Internet-of-Things world on the one side and centralised cloud data centres on the other. Edge clusters could consist of small single-board devices, which are widely used in different applications. This includes microcontrollers regulating an industrial process or controllers monitoring and managing traffic roadside. Although generally hardware capabilities of devices are growing, resources at the edge are often still limited, requiring an intelligent resource management. This can done through self-adaptive scaling mechanisms in these clusters allowing to scale components of the application in the cluster. We introduce here an auto-scalable cluster architecture for lightweight container-based edge devices. A serverless architecture forms the core of this architecture. The auto-scaling component is based on fuzzy logic in order to better manage uncertainty problems arising in such contexts. Our evaluations show that our platform architecture and the auto-scaling functionality satisfy the need of lightweightness in edge architecture.

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“…The approach is starting from Web resources, content with a high level of abstraction is obtained: concepts, connections between concepts, and instance-population are identified and arranged into ontology. Fang et al [12] proposed an agility-oriented and fuzziness-embedded cloud-service ontology model, which adopted agility-centric design. The model enabled comprehensive service specification by capturing cloud concept details and their interactions.…”

Section: Ontology Learningmentioning

confidence: 99%

Specificity-Aware Ontology Generation for Improving Web Service Clustering

Rupasingha

Paik

Kumara

2018

IEICE Trans. Inf. & Syst.

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SUMMARYWith the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, Fmeasure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms.

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An agility-oriented and fuzziness-embedded semantic model for collaborative cloud service search, retrieval and recommendation

Cited by 28 publications

References 31 publications

Leveraging cloud computing for the semantic web: review and trends

Leveraging cloud computing for the semantic web: review and trends

Fuzzy Container Orchestration for Self-adaptive Edge Architectures

Specificity-Aware Ontology Generation for Improving Web Service Clustering

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