Next generation applications such as augmented/vir-tual reality, autonomous driving, and
Industry 4.0, have tight latency constraints and produce large amounts of data. To address …

Applications that process streams of events generated by sensors are important in a wide
range of domains. It is now common to distribute stream processing across edge devices …

Stream Processing Engines (SPEs) traditionally de-ploy applications on a set of shared
workers (eg, threads, processes, or containers) requiring complex performance man …

Deploying stream computing applications on edge networks brings a new set of challenges
including frequent reconfigurations due to client mobility and topology changes …

In stream processing, data is streamed as a continuous flow of data items, which are
generated from multiple sources and geographical locations. The common approach for …

Data Stream Processing (DSP) is a widely used programming paradigm to process an
unbounded event stream. Often, DSP frameworks are deployed on the cloud with a scalable …

Due to the long-run and unpredictable nature of stream processing, any statically
configuredexecution of stream jobs fails to process data in a timely and efficient manner. To …

Real-time data processing is a standard requirement in Fog Computing. Dynamically
adapting data stream processing frameworks is an essential functionality to handle time …

There is increasing demand for handling massive amounts of data in a timely manner via
Distributed Stream Processing (DSP). A DSP application is often structured as a directed …

Distributed Stream Processing (DSP) applications are increasingly used in new pervasive
services that process enormous amounts of data in a seamless and near real-time fashion …