Under several emerging application scenarios, such as in smart cities, operational
monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous …

In a stream data analytics system, input data arrive continuously and trigger the processing
and updating of analytics results. We focus on applications with real-time constraints, in …

Many applications in several domains such as telecommunications, network security, large-
scale sensor networks, require online processing of continuous data flows. They produce …

Over the last decade, several interconnected disruptions have happened in the large scale
distributed and parallel computing landscape. The volume of data currently produced by …

Elastic scaling allows a data stream processing system to react to a dynamically changing
query or event workload by automatically scaling in or out. Thereby, both unpredictable load …

The interest in processing data events under stringent time constraints as they arrive has led
to the emergence of architecture and engines for data stream processing. Edge computing …

The popularity and proliferation of the Internet of Things (IoT) paradigm is resulting in a
growing number of devices connected to the Internet. These devices are generating and …

Stream processing is an emerging paradigm to handle data streams upon arrival, powering
latency-critical application such as fraud detection, algorithmic trading, and health …

Systems enabling the continuous processing of large data streams have recently attracted
the attention of the scientific community and industrial stakeholders. Data Stream Processing …

The era of big data has led to the emergence of new real-time distributed stream processing
engines like Apache Storm. We present Stela (STream processing ELAsticity), a stream …