Advanced analysis of data streams is quickly becoming a key area of data mining research
as the number of applications demanding such processing increases. Online mining when …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

We present a new approach for dealing with distribution change and concept drift when
learning from data sequences that may vary with time. We use sliding windows whose size, …

An emerging problem in Data Streams is the detection of concept drift. This problem is
aggravated when the drift is gradual over time. In this work we define a method for detecting …

As long as virtualization has been introduced in data centers, it has been opening new
chances for resource management. Nowadays, it is not just used as a tool for consolidating …

Graph mining is a challenging task by itself, and even more so when processing data streams
which evolve in real-time. Data stream mining faces hard constraints regarding time and …

We propose and illustrate a method for developing algorithms that can adaptively learn from
data streams that drift over time. As an example, we take Hoeffding Tree, an incremental …

As energy-related costs have become a major economical factor for IT infrastructures and
data-centers, companies and the research community are being challenged to find better and …

In this paper we introduce a new approach to the consolidation strategy of a data center that
allows an important reduction in the amount of active nodes required to process a …

Adaptive techniques based on machine learning and data mining are gaining relevance in
self-management and self-defense for networks and distributed systems. In this paper, we …