  The massive amount of current data has led to many different forms of data
analysis processes that aim to explore this data to uncover valuable insights.
Methodologies to guide the development of big data science projects, including
CRISP-DM and SEMMA, have been widely used in industry and academia. The data
analysis modeling phase, which involves decisions on the most appropriate
models to adopt, is at the core of these projects. However, from a software
engineering perspective, the design and automation of activities performed in
this phase are challenging. In this paper, we propose an approach to the data
analysis modeling process which involves (i) the assessment of the variability
inherent in the CRISP-DM data analysis modeling phase and the provision of
feature models that represent this variability; (ii) the definition of a
framework structural design that captures the identified variability; and (iii)
evaluation of the developed framework design in terms of the possibilities for
process automation. The proposed approach advances the state of the art by
offering a variability-aware design solution that can enhance system
flexibility, potentially leading to novel software frameworks which can
significantly improve the level of automation in data analysis modeling
process.

Software Engineering