World is generating immeasurable amount of data every minute, that needs to be analyzed
for better decision making. In order to fulfil this demand of faster analytics, businesses are …

Last decade demonstrate the massive growth in organizational data which keeps on
increasing multi‐fold as millions of records get updated every second. Handling such vast …

The availability of time series streaming data has increased dramatically in recent years.
Since the last decade, there has been a growing interest in learning from real-time data …

Abstract Machine learning has been widely applied to handle big data. In real-world
applications, data are in the form of streams. Streaming data bring new challenges to …

The digital universe is growing rapidly. The volume of data generated per annum is in the
order of zeta bytes due to the proliferation of the Internet. Many real-world applications …

Abstract Concept drift is one of the most prominent issues in streaming data that machine
learning models need to address. Most of the research in the field of concept drift targets …

In data stream mining, one of the biggest challenges is to develop algorithms that deal with
the changing data. As data evolve over time, static models become outdated. This …

Nowadays the large volume of data from different sources especially as streaming data
opens us various opportunities for streaming analytics. Concept drift is one of the …

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

Mining and analysing streaming data is crucial for many applications, and this area of
research has gained extensive attention over the past decade. However, there are several …