Ontology-based Design of Experiments on Big Data Solutions

In this paper the ontology-based approach is proposed to support the evaluation of big data systems. Firstly, the approach formalises a decomposition and recombination of the big data solution, allowing for the aggregation of component evaluation results at inter-component level. Secondly, existing work on Design of Experiments (DoE) is translated into an ontology for supporting the selection of experiments. It exploits domain and inter-domain specic restrictions on the factor combinations in order to select from the very large number of possible experiments a representative subset. Contrary to existing approaches, the proposed use of ontologies is not limited to the assertional description and exploitation of past experiments but offers richer terminological descriptions for the development of a DoE from scratch. As an application example, a DoE is developed for a maritime big data solution