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 …

Systematic literature reviews (SLRs) are a central part of evidence-based research, which
involves collecting and integrating empirical evidence on specific research questions. A key …

An ever increasing number of services requires real-time analysis of collected data streams.
Emerging Fog/Edge computing platforms are appealing for such latency-sensitive …

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 …

Big data attracts researchers and practitioners around the globe in their desire to effectively
manage the data deluge resulting from the ongoing evolution of the information systems …

Sensory data profoundly influences the quality of detected events in a distributed complex
event processing system (DCEP). Since each sensor's status is unstable at runtime, a single …

One of the most important issues in distributed data stream processing systems is using
operator migration to handle highly variable workloads cost-efficiently and adapt to the …

Purpose As the global freight transport network has experienced high vulnerability and
threats from both natural and man-made disasters, as a result, a huge amount of data is …

With the proliferation of Internet of Things (IoT) devices, real-time stream processing at the
edge of the network has gained significant attention. However, edge stream processing …

The emergence of edge stream processing has created a new way of processing real-time
data from the Internet of Things (IoT), which comprises a plethora of geographically …