Traditional parallel computing for power management systems has prime challenges such
as execution time, computational complexity, and efficiency like process time and delays in …

Under several emerging application scenarios, such as in smart cities, operational
monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous …

Smart grids have been gradually replacing the traditional power grids since the last decade.
Such transformation is linked to adding a large number of smart meters and other sources of …

Running computer vision algorithms on images or videos collected by mobile devices
represent a new class of latency-sensitive applications that expect to benefit from edge …

Many Internet of Things (IoT) applications would benefit if streams of data could be analyzed
rapidly at the Edge, near the data source. However, existing Stream Processing Engines …

In the last few years, a large number of real-time analytics applications rely on the Data
Stream Processing (DSP) so to extract, in a timely manner, valuable information from …

Over the last decade, several interconnected disruptions have happened in the large scale
distributed and parallel computing landscape. The volume of data currently produced by …

Stream processing is an emerging paradigm to handle data streams upon arrival, powering
latency-critical application such as fraud detection, algorithmic trading, and health …

We are currently living in the big data era, in which it has become more necessary than ever
to develop “smart” schedulers. It is common knowledge that the default Storm scheduler, as …

The emergence of social media, the worldwide web, electronic transactions, and next-
generation sequencing not only opens new horizons of opportunities but also leads to the …