Contemporary solutions for cloud-supported, edge-data analytics mostly apply analytics
techniques in a rigid bottom-up approach, regardless of the data's origin. Typically, data are …

The exponential increase of the data generated by pervasive and mobile devices requires
disrupting approaches for the realization of emerging mobile and IoT applications. Although …

Advances in sensor technology, personal mobile devices, wireless broadband
communications, and Cloud computing are enabling real-time collection and dissemination …

Serverless computing has recently attracted a lot of attention from research and industry due
to its promise of ultimate elasticity and operational simplicity. However, there is no …

Edge computing is a distributed computing paradigm such that client data are processed at
the periphery of the network, as close as possible to the originating source. Since the 21st …

Now we are entering the era of the Internet of Everything (IoE) and billions of sensors and
actuators are connected to the network. As one of the most sophisticated IoE applications …

This paper discusses approaches and environments for carrying out analytics on Clouds for
Big Data applications. It revolves around four important areas of analytics and Big Data …

Today, patients are demanding a newer and more sophisticated health care system, one
that is more personalized and matches the speed of modern life. For the latency and energy …

A large number of cloud middleware platforms and tools are deployed to support a variety of
internet‐of‐things (IoT) data analytics tasks. It is a common practice that such cloud …

The advent of open-source serverless computing frameworks has introduced the ability to
bring the Functions-as-a-Service (FaaS) paradigm for applications to be executed on …