Applications that generate data in the form of fast streams from non-stationary environments,
that is, those where the underlying phenomena change over time, are becoming …

The volume of data is rapidly increasing due to the development of the technology of
information and communication. This data comes mostly in the form of streams. Learning …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Learning from data streams is an increasingly important topic in data mining, machine
learning, and artificial intelligence in general. A major focus in the data stream literature is …

We are living in the age of big data, a majority of which is stream data. The real-time
processing of this data requires careful consideration from different perspectives. Concept …

The big data paradigm has posed new challenges for the Machine Learning algorithms,
such as analysing continuous flows of data, in the form of data streams, and dealing with the …

We live in the age when the speed and amounts of data produced are enormous. According
to a recent IDC report [59] the data generated in 2014 is estimated to be 4.4 zettabytes …

The relationship between the input and output data changes over time refer to as concept
drift, which is a major problem in online learning due to its impact on the predictive …

Over the past decades, tremendous technological advancement has massively increased
the numerous applications such as financial market analysis, email systems, weather …

Learning from data streams in the presence of concept drift is among the biggest challenges
of contemporary machine learning. Algorithms designed for such scenarios must take into …