The arising Internet of Things (IoT) will require significant changes to current stream
processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present …

Transactional stream processing engines (TSPEs) differ significantly in their designs, but all
rely on non-adaptive scheduling strategies for processing concurrent state transactions …

Data stream compression attracts much attention recently due to the rise of IoT applications.
Thanks to the balanced computational power and energy consumption, asymmetric …

Transactional stream processing (TSP) strives to create a cohesive model that merges the
advantages of both transactional and stream-oriented guarantees. Over the past decade …

Data stream processing systems (DSPSs) enable users to express and run stream
applications to continuously process data streams. To achieve realtime data analytics …

Recent emergencies, such as the COVID-19 pandemic have shown how timely information
sharing is essential to promptly and effectively react to emergencies. Internet of Things has …

Data streaming applications in Cyber-Physical Systems enable high-throughput, low-latency
transformations of raw data into value. The performance of such applications, run by Stream …

Edge Computing architecture provides computing capacity closer to users to fulfill the
requirements of Future Internet services and applications, such as low latency. However, as …

In the burgeoning realm of Internet of Things (IoT) applications on edge devices, data stream
compression has become increasingly pertinent. The integration of added compression …

Stateful scheduling is of critical importance for the performance of a distributed stream
computing system. In such a system, inappropriate task deployment lowers the resource …