Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

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.