  The complexity of database systems has increased significantly along with the
continuous growth of data, resulting in NoSQL systems and forcing Information
Systems (IS) architects to constantly adapt their data models (i.e., the data
structure of information stored in the database) and carefully choose the best
option(s) for storing and managing data. In this context, we propose %in this
paper an automatic global approach for leading data models' transformation
process. This approach starts with the generation of all possible solutions. It
then relies on a cost model that helps to compare these generated data models
in a logical level to finally choose the best one for the given use case. This
cost model integrates both data model and queries cost. It also takes into
consideration the environmental impact of a data model as well as its financial
and its time costs. This work presents for the first time a multidimensional
cost model encompassing time, environmental and financial constraints, which
compares data models leading to the choice of the optimal one for a given use
case. In addition, a simulation for data model's transformation and cost
computation has been developed based on our approach.
