The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

The prevalence of mobile phones, the internet-of-things technology, and networks of
sensors has led to an enormous and ever increasing amount of data that are now more …

Every day, huge volumes of sensory, transactional, and web data are continuously
generated as streams, which need to be analyzed online as they arrive. Streaming data can …

Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

An increasing number of real-world applications are associated with streaming data drawn
from drifting and nonstationary distributions that change over time. These applications …

In todays digital era, a massive amount of streaming data is automatically and continuously
generated. To learn such data streams, many algorithms have been proposed during the …

In streaming data classification, most of the existing methods assume that all arrived
evolving data are completely labeled. One challenge is that some applications where only …

Recent advances in storage and processing have provided the possibility of automatic
gathering of information, which in turn leads to fast and continuous flows of data. The data …

Safe and reliable operation of systems relies on the use of online condition monitoring and
diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Model …