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 …

The concept of state and its applications vary widely across big data processing systems.
This is evident in both the research literature and existing systems, such as Apache Flink …

High-volume data streams are straining the limits of stream processing frameworks which
need advanced parallel processing capabilities to withstand the actual incoming bandwidth …

The intra-window join (IaWJ), ie, joining two input streams over a single window, is a core
operation in modern stream processing applications. This paper presents the first …

Distributed Complex Event Processing (DCEP) is a paradigm to infer the occurrence of
complex situations in the surrounding world from basic events like sensor readings. In doing …

In the world of Big Data analytics, there is a series of tools aiming at simplifying
programming applications to be executed on clusters. Although each tool claims to provide …

We present a container-based architecture for supporting autonomic data stream processing
application on fog computing infrastructures. Our architecture runs applications as Docker …

Computing aggregation over sliding windows, ie, finite subsets of an unbounded stream, is a
core operation in streaming analytics. We propose PBA (Parallel Boundary Aggregator), a …

In the stream processing domain, applications are represented by graphs of operators
arbitrarily connected and filled with their business logic code. The APIs of existing Stream …

The emergence of the Internet of Things (IoT) data stream applications has posed a number
of new challenges to existing infrastructures, processing engines, and programming models …