Big data processing systems are evolving to be more stream oriented where each data
record is processed as it arrives by distributed and low-latency computational frameworks on …

Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby
massive amounts of heterogeneous data are gathered from multiple sources, managed …

The concept of event processing is established as a generic computational paradigm in
various application fields. Events report on state changes of a system and its environment …

Big Data analytics for storing, processing, and analyzing large-scale datasets has become
an essential tool for the industry. The advent of distributed computing frameworks such as …

The increasing need for real-time insights in data sparked the development of multiple
stream processing frameworks. Several benchmarking studies were conducted in an effort to …

Cloud-native applications constitute a recent trend for designing large-scale software
systems. However, even though several cloud-native tools and patterns have emerged to …

The Internet of Things (IoT) presents a novel computing architecture for data management: a
distributed, highly dynamic, and heterogeneous environment of massive scale. Applications …

Distributed stream processing engines are designed with a focus on scalability to process
big data volumes in a continuous manner. We present the Theodolite method for …

Scale-out stream processing engines (SPEs) are powering large big data applications on
high velocity data streams. Industrial setups require SPEs to sustain outages, varying data …

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