Real-Time Data Mining Discussion

I am about to prepare a paper concerning the field of real-time data mining. Real-time here means the process of incremental training of an existing model as soon as the data arrives.

There is a number of papers introducing algorithms for incremental association analysis, incremental clustering etc. Stream mining ís a field which is closely related to that. The main reason for the implementation of incremental algorithms is a) the large amount of data to be mined and b) the high rate of new data that is evolving every day.

Using classical batch mining algorithms, models that are outdated for some reason, would have to be re-trained, which could be very time consuming for billions of records. And once the training is completed, the training would have to be restarted once again because a bulk of new data has been arrived.

The question that I would like to discuss now is: For what real world applications would it be a meaningful or even essential to use real-time training of models?

Two main reasons could determine the answer to that question:

You just want to incorporate new data into existing models in order to increase the prediction accuracy of your model or
Your underlying data is subject to more or less massive changes (also refered to as concept drift) and you want to adapt your mining model continuously to that reality.

I'm looking for some examples or ideas where one of these cases apply and it would be a good idea to have incremental mining algorithms involved.

I'm looking forward to inspiring some discussion on that issue.