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 (SP) has evolved as the leading paradigm to process and gain value
from the high volume of streaming data produced, eg, in the domain of the Internet of Things …

High-level parallel programming is an active research topic aimed at promoting parallel
programming methodologies that provide the programmer with high-level abstractions to …

The past decade has seen rapid growth of distributed stream data processing systems.
Under these systems, a stream application is realized as a Directed Acyclic Graph (DAG) of …

It is often a challenge to keep input/output tasks/results in order for parallel computations
over data streams, particularly when stateless task operators are replicated to increase …

Abstract Paradigms like Internet of Things and the most recent Internet of Everything are
shifting the attention towards systems able to process unbounded sequences of items in the …

The unpredictable variability of Data Stream Processing (DSP) application workloads calls
for advanced mechanisms and policies for elastically scaling the processing capacity of DSP …

The ability to promptly and efficiently detect arbitrarily complex patterns in massive real-time
data streams is a crucial requirement in many modern applications. The ever-growing scale …

This paper addresses the shared resource contention problem associated with the auto-
parallelization of running queries in distributed stream processing engines. In such …

Ensuring relative consistency in executing temporal queries to access real-time sensor data
streams maintained in a database is a challenging problem, particularly when data …