  More and more, with the growing focus on large scale analytics, we are
confronted with the need of integrating data from multiple sources. The problem
is that these data are impossible to reuse as-is. The net result is high cost,
with the further drawback that the resulting integrated data will again be
hardly reusable as-is. iTelos is a general purpose methodology aiming at
minimizing the effects of this process. The intuition is that data will be
treated differently based on their popularity: the more a certain set of data
have been reused, the more they will be reused and the less they will be
changed across reuses, thus decreasing the overall data preprocessing costs,
while increasing backward compatibility and future sharing
