  Big data solutions are designed to cope with data of huge Volume and wide
Variety, that need to be ingested at high Velocity and have potential Veracity
issues, challenging characteristics that are usually referred to as the "4Vs of
Big Data". In order to evaluate possibly complex big data solutions, stress
tests require to assess a large number of combinations of sub-components
jointly with the possible big data variations. A formalization of the Design of
Experiments (DoE) on big data solutions is aimed at ensuring the
reproducibility of the experiments, facilitating their partitioning in
sub-experiments and guaranteeing the consistency of their outcomes in a global
assessment. In this paper, an ontology-based approach is proposed to support
the evaluation of a big data system in two ways. Firstly, the approach
formalizes 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 DoE is translated into an ontology for supporting
the selection of experiments. The proposed ontology-based approach offers the
possibility to combine knowledge from the evaluation domain and the application
domain. It exploits domain and inter-domain specific restrictions on the factor
combinations in order to reduce the number of experiments. 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 maritime big data solution to the problem of detecting
and predicting vessel suspicious behaviour through mobility analysis is
selected. The article is concluded with a sketch of future works.
