So, I have not been able to find any literature on this subject but it seems like something worth giving a thought:
What are the best practices in model training and optimization if new observations are available?
Is there any way to determine the period/frequency of re-training a model before the predictions begin to degrade?
Is it over-fitting if the parameters are re-optimised for the aggregated data?
Note that the learning may not necessarily be online. One may wish to upgrade an existing model after observing significant variance in more recent predictions.