Mining data streams has been a focal point of research interest over the past decade.
Hardware and software advances have contributed to the significance of this area of …

With the advance in both hardware and software technologies, automated data generation
and storage has become faster than ever. Such data is referred to as data streams …

In the last two decades, machine learning research and practice has focused on batch
learning, usually with small datasets. Nowadays there are applications in which the data are …

Machine learning has traditionally been solely performed on servers and high-performance
machines. However, advances in chip technology have given us miniature libraries that fit in …

Privacy is becoming an increasingly important issue in many data-mining applications. This
has triggered the development of many privacy-preserving data-mining techniques. A large …

Advances in computing and communication over wired and wireless networks have resulted
in many pervasive distributed computing environments. The Internet, intranets, local area …

This paper presents an overview of an experimental mobile and distributed data stream
mining system that allows real time vehicle-health monitoring and driver characterization. It …

The government and industry have given the recent development of the Internet of Things in
the healthcare sector significant respect. Health service providers retain data gathered from …

With the proliferation of applications generating vast volumes of data streams, numerous
clustering methods have emerged to process and extract valuable insights from this data …

Causality analysis continues to remain one of the fundamental research questions and the
ultimate objective for a tremendous amount of scientific studies. In line with the rapid …