Cloud Description Ontology for Service Discovery and Selection

Rekik, Molka; Boukadi, Khouloud; Ben-Abdallah, Hanêne

doi:10.5220/0005556400260036

Cited by 22 publications

(18 citation statements)

References 14 publications

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“…For more details about CSO, we refer the reader to [7]. In this section, we present the important CSO's concepts and axioms.…”

Section: Cloud Service Ontology: Csomentioning

confidence: 99%

“…The precision measures the ratio of correctly found anti-patterns over the total number of detected anti-patterns. The last column indicates the recall which infers the ratio of correctly found anti-patterns 7 https://jena.apache.org/ over the total number of proposed anti-patterns. Results from Table 3 are encouraging.…”

Section: Algorithm Evaluationmentioning

confidence: 99%

“…To optimize the effort, it is better to reuse and benefit from the existing reasoners and cover their limitations by a set of antipatterns. In order to evaluate our CSO [7], we combine, in this work, the two evaluation techniques, the reasoner and anti-patterns, to identify and correct the redundancy, the incompleteness and the domain knowledge missing of the ontology.…”

Section: Figure 1 the Top Level Conceptsmentioning

confidence: 99%

“…In this paper, we define a novel ontology evaluation approach which considers the cloud domain to enhance the previously proposed CSO quality [7]. We denote that our approach is the building block for an adequate service discovery.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, cloud providers describe similar services in different manners (different pricing policies, different quality of services, etc.). This motivated the proposition to standardize the description of cloud services by introducing ontologies [3][11] [7]. However, the absence of an evaluation of the so-far proposed ontologies did not encourage their adoption.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

Rekik

Boukadi

Gaaloul³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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The lack of standardized descriptions of cloud services hinders their discovery. In an effort to standardize cloud service descriptions, several works propose to use ontologies. Nevertheless, the adoption of any of the proposed ontologies calls for an evaluation to show its efficiency in cloud service discovery. Indeed, the existing cloud providers describe, their similar offered services in different ways. Thus, various existing works aim at standardizing the representation of cloud computing services by proposing ontologies. Since the existing proposals were not evaluated, they might be less adopted and considered. Indeed, the ontology evaluation has a direct impact on its understandability and reusability. In this paper, we propose an evaluation approach to validate our proposed Cloud Service Ontology (CSO), to guarantee an adequate cloud service discovery. To this end, this paper has a three-fold contribution. First, we specify a set of patterns and anti-patterns in order to evaluate our CSO. Second, we define an antipattern detection algorithm based on SPARQL queries which provides a set of correction recommendations to help ontologists revise their ontology. Finally, tests were conducted in relation to: (i) the algorithm efficiency and (ii) anti-pattern detection of design anomalies as well as taxonomic and domain errors within CSO.

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“…For more details about CSO, we refer the reader to [7]. In this section, we present the important CSO's concepts and axioms.…”

Section: Cloud Service Ontology: Csomentioning

confidence: 99%

Section: Algorithm Evaluationmentioning

confidence: 99%

Section: Figure 1 the Top Level Conceptsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

Rekik

Boukadi

Gaaloul³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Linked USDL Extension for Cloud Services Description

Nabli

Djemaa

Amous

2019

Lecture Notes in Computer Science

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Ontology-Based Resource Allocation for Internet of Things

Nezami

Zamanifar

Arena

et al. 2019

IFIP Advances in Information and Communication Technology

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The Internet of Things has revolutionized the lifestyle in all aspects. Considering the huge number of connected objects and the plethora of real-time services, edge computing approaches have emerged. Resource allocation is one of the most important challenges in the Internet of Things. Here edge computing allows the use of resources at the edge network, hence, filling the gap between cloud and end-devices. The network resource allocation should meet users' expectations and provide optimal use of resources. Today, most of the systems are moving toward a self-x concept, such as self-organizing. As a result, these systems must be aware of users' preferences and the current state of the IoT ecosystem in order to adapt themselves to the conditions. In this context, we benefit from the employment of semantic technologies as these enhance the current systems with information modeling and reasoning capabilities which effectively support the allocation of IoT network resources.

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Cloud Description Ontology for Service Discovery and Selection

Cited by 22 publications

References 14 publications

Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

Linked USDL Extension for Cloud Services Description

Ontology-Based Resource Allocation for Internet of Things

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