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 …

Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

Fog computing overcomes the limitations of executing Internet of Things (IoT) applications in
remote Cloud datacentres by extending the computation facilities closer to data sources …

Data Stream Processing (DSP) applications analyze data flows in near real-time by means
of operators, which process and transform incoming data. Operators handle high data rates …

As real‐time and immediate feedback becomes increasingly important in tasks related to
mobile information, big data stream processing systems are increasingly applied to process …

This paper focuses on SDN-based approaches for deploying stream processing workloads
on heterogeneous environments comprising wide-area networks, cloud and fog resources …

The continual increase in the amount of generated data by social media, IoT devices, and
monitoring systems have motivated the use of Distributed Data Stream Processing (DSP) …

Deep Neural Networks (DNNs) may be partitioned across the edge and the cloud to improve
the performance efficiency of inference. DNN partitions are determined based on …

The constant technological advances bring new devices to the market every day. Due to this,
heterogeneous Wireless Sensor Network (WSN) are common scenarios in many …

High performance distributed systems such as distributed stream processing systems and
message-passing parallel programs are often deployed on platforms that make use of …