Creating a semantically-enhanced cloud services environment through ontology evolution

Rodríguez-García, Miguel Ángel; Valencia‐García, Rafael; García‐Sánchez, Francisco; Zapater, José Javier Samper

doi:10.1016/j.future.2013.08.003

Cited by 45 publications

(16 citation statements)

References 32 publications

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“…4 demonstrate the extensions AoFeCSO achieved in contrast to other existing models (i.e. [8,9,10,11,12,31]). …”

Section: Agility-centric Designmentioning

confidence: 79%

“…Existing models [4,8,9,10,11,12,32] tend to ignore the many relationships among cloud computing concept. Firstly, the interoperability between CSPs and cloud services is often inexplicitly expressed.…”

Section: Explicit Cloud Service and Concept Relationship Assertionmentioning

confidence: 99%

“…4, AoFeCSO utilizes annotation properties in a rather different approach against others [8,34,35] for entity annotations. It involves annotating not only cloud services, but all other concepts in the ontology, e.g.…”

Section: Multi-sourced Annotation Property Assertionmentioning

confidence: 99%

“…The semantic platform for cloud service annotation and retrieval [8] utilizes multiple ontologies of different domains. Being advanced in its annotation term extraction and indexing techniques plus the integrated ontology evolution module, it can implement ontology updates according to the service concept information found on Wikipedia.…”

Section: Ontology-based Knowledge Representation On Web Services and mentioning

confidence: 99%

“…Nevertheless, most of the models are primarily designed to work for certain limited service categories: e.g. infrastructure services [10,11,12,32], platform services [9,56] and software services [8,34]. FCFA [10], for instance, is a hierarchical federated resource exploration and sharing framework model which drives federated cloud cooperation and eliminates interoperability issues among independent organizations and providers.…”

Section: Ontology-based Knowledge Representation On Web Services and mentioning

confidence: 99%

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

Fang

Liu

Romdhani

et al. 2016

Future Generation Computer Systems

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“…4 demonstrate the extensions AoFeCSO achieved in contrast to other existing models (i.e. [8,9,10,11,12,31]). …”

Section: Agility-centric Designmentioning

confidence: 79%

Section: Explicit Cloud Service and Concept Relationship Assertionmentioning

confidence: 99%

Section: Multi-sourced Annotation Property Assertionmentioning

confidence: 99%

Section: Ontology-based Knowledge Representation On Web Services and mentioning

confidence: 99%

Section: Ontology-based Knowledge Representation On Web Services and mentioning

confidence: 99%

See 3 more Smart Citations

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

Fang

Liu

Romdhani

et al. 2016

Future Generation Computer Systems

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Architecting cloud‐enabled systems: a systematic survey of challenges and solutions

2016

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SUMMARYThe literature on the challenges of and potential solutions to architecting cloud-based systems is rapidly growing but is scattered. It is important to systematically analyze and synthesize the existing research on architecting cloud-based software systems in order to build a cohesive body of knowledge of the reported challenges and solutions. We have systematically identified and reviewed 133 papers that report architecture-related challenges and solutions for cloud-based software systems. This paper reports the methodological details, findings, and implications of a systematic review that has enabled us to identify 44 unique categories of challenges and associated solutions for architecting cloud-based software systems. We assert that the identified challenges and solutions classified into the categories form a body of knowledge that can be leveraged for designing or evaluating software architectures for cloud-based systems. Our key conclusions are that a large number of primary studies focus on middleware services aimed at achieving scalability, performance, response time, and efficient resource optimization. Architecting cloud-based systems presents unique challenges as the systems to be designed range from pervasive embedded systems and enterprise applications to smart devices with Internet of Things. We also conclude that there is a huge potential of research on architecting cloud-based systems in areas related to green computing, energy efficient systems, mobile cloud computing, and Internet of Things.

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CollaborativeHealth: Smart Technologies to Surveil Outbreaks of Infectious Diseases Through Direct and Indirect Citizen Participation

Apolinario-Arzube

García-Díaz

Pinto

et al. 2020

Advances in Intelligent Systems and Computing

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Creating a semantically-enhanced cloud services environment through ontology evolution

Cited by 45 publications

References 32 publications

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

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

Architecting cloud‐enabled systems: a systematic survey of challenges and solutions

CollaborativeHealth: Smart Technologies to Surveil Outbreaks of Infectious Diseases Through Direct and Indirect Citizen Participation

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