The ravages of concept drift in stream learning applications and how to deal with it - KDnuggets

The Big Data paradigm has gained momentum last decade, because of its promise to deliver valuable insights to many real-world applications. With the advent of this emerging paradigm comes not only an increase in the volume of available data, but also the notion of its arrival velocity, that is, these real-world applications generate data in real-time at rates faster than those that can be handled by traditional systems. This situation leads us to assume that we have to deal with a potentially infinite and ever-growing datasets that may arrive continuously (stream learning) in batches of instances or instance by instance, in contrast to traditional systems where there is free access to all historical data. These traditional processing systems assume that data are at rest and simultaneously accessed. The models based on this traditional processing do not continuously integrate new information into already constructed models but, instead, regularly reconstruct new models from the scratch.