  The distributed Grid infrastructure for High Energy Physics experiments at
the Large Hadron Collider (LHC) in Geneva comprises a set of computing centres,
spread all over the world, as part of the Worldwide LHC Computing Grid (WLCG).
In Italy, the Tier-1 functionalities are served by the INFN-CNAF data center,
which provides also computing and storage resources to more than twenty non-LHC
experiments. For this reason, a high amount of logs are collected each day from
various sources, which are highly heterogeneous and difficult to harmonize. In
this contribution, a working implementation of a system that collects, parses
and displays the log information from CNAF data sources and the investigation
of a Machine Learning based predictive maintenance system, is presented.
