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 …

Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …

Distributed stream processing frameworks are designed to perform continuous computation
on possibly unbounded data streams whose rates can change over time. Devising solutions …

Stream processing has become the dominant processing model for monitoring and real-time
analytics. Modern Parallel Stream Processing Engines (pSPEs) have made it feasible to …

Abstract Distributed Stream Processing Systems (DSPS) are “Fast Data” platforms that allow
streaming applications to be composed and executed with low latency on commodity …

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 …

Low latency stream processing on large clusters consisting of hundreds to thousands of
servers is an increasingly important challenge. A crucial barrier to tackling this challenge is …

This paper focused on online scheduling of distributed Earth observation satellite system in
a dynamic environment. The objective was to maximize the total profit of the overall system …

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 …