AntiPattren-based cloud ontology evaluation

Nowadays, cloud computing is an emerging technology thanks to its ability to provide on-demand computing services (hardware and software) with less description standardization effort. Multiple issues and challenges in discovering cloud services appear due to the lack of the cloud service description standardization. In fact, 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 while proposing ontologies. However, 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. This paper contribution is threefold. First, it specifies a set of patterns and anti-patterns in order to evaluate CSO. Second, it defines an anti-pattern detection method based on SPARQL queries which provides a set of correction recommendations to help ontologists revise the ontology. Finally, some experiment tests were conducted in relation to: (i) the method efficiency and (ii) anti-pattern detection of design anomalies as well as taxonomic and domain errors within CSO.