Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …

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

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 …

Abstract Data Stream Processing (DSP) applications are widely used to develop new
pervasive services, which require to seamlessly process huge amounts of data in a near real …

An important as well as challenging task in modern applications is the management and
processing with very short delays of large data volumes. It is quite often, that the capabilities …

A recurrent challenge in real‐world applications is autonomous management of the
executions at run‐time. In this vein, stream processing is a class of applications that compute …

The proliferation of Internet-of-Things (IoT) devices is rapidly increasing the demands for
efficient processing of low latency stream data generated close to the edge of the network …

Nowadays, there is an accelerating need to efficiently and timely handle large amounts of
data that arrives continuously. Streams of big data led to the emergence of several …

Data Stream Processing (DSP) has emerged as a key enabler to develop pervasive services
that require to process data in a near real-time fashion. DSP applications keep up with the …

A recent trend in stream processing is offloading the computation of decomposable
aggregation functions (DAF) from cloud nodes to geo-distributed fog/edge devices to …