  Data sets are growing in complexity thanks to the increasing facilities we
have nowadays to both generate and store data. This poses many challenges to
machine learning that are leading to the proposal of new methods and paradigms,
in order to be able to deal with what is nowadays referred to as Big Data. In
this paper we propose a method for the aggregation of different Bayesian
network structures that have been learned from separate data sets, as a first
step towards mining data sets that need to be partitioned in an horizontal way,
i.e. with respect to the instances, in order to be processed. Considerations
that should be taken into account when dealing with this situation are
discussed. Scalable learning of Bayesian networks is slowly emerging, and our
method constitutes one of the first insights into Gaussian Bayesian network
aggregation from different sources. Tested on synthetic data it obtains good
results that surpass those from individual learning. Future research will be
focused on expanding the method and testing more diverse data sets.
