Runtime adaptation of data stream processing systems: The state of the art
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 …
the analysis of continuous and fast information flows, which often have to be processed with …
A systematic map** of performance in distributed stream processing systems
Several software systems are built upon stream processing architectures to process large
amounts of data in near real-time. Today's distributed stream processing systems (DSPSs) …
amounts of data in near real-time. Today's distributed stream processing systems (DSPSs) …
Big data stream processing
This chapter provides students, industry experts, and researchers a high-level and
comprehensive overview of the end-to-end architectures of big data stream processing …
comprehensive overview of the end-to-end architectures of big data stream processing …
A configurable method for benchmarking scalability of cloud-native applications
S Henning, W Hasselbring - Empirical Software Engineering, 2022 - Springer
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 …
systems. However, even though several cloud-native tools and patterns have emerged to …
[HTML][HTML] Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
S Henning, W Hasselbring - Journal of Systems and Software, 2024 - Elsevier
Context: The combination of distributed stream processing with microservice architectures is
an emerging pattern for building data-intensive software systems. In such systems, stream …
an emerging pattern for building data-intensive software systems. In such systems, stream …
Darwin: An online deep learning approach to handle concept drifts in predictive process monitoring
Predictive process monitoring (PPM) is a specific task under the umbrella of Process Mining
that aims to predict several factors of a business process (eg, next activity prediction) based …
that aims to predict several factors of a business process (eg, next activity prediction) based …
[HTML][HTML] A reference architecture for serverless big data processing
Despite significant advances in data management systems in recent decades, the
processing of big data at scale remains very challenging. While cloud computing has been …
processing of big data at scale remains very challenging. While cloud computing has been …
How to measure scalability of distributed stream processing engines?
S Henning, W Hasselbring - Companion of the ACM/SPEC international …, 2021 - dl.acm.org
Scalability is promoted as a key quality feature of modern big data stream processing
engines. However, even though research made huge efforts to provide precise definitions …
engines. However, even though research made huge efforts to provide precise definitions …
A performance analysis of fault recovery in stream processing frameworks
G van Dongen, D Van Den Poel - IEEE Access, 2021 - ieeexplore.ieee.org
Distributed stream processing frameworks have gained widespread adoption in the last
decade because they abstract away the complexity of parallel processing. One of their key …
decade because they abstract away the complexity of parallel processing. One of their key …
Clonos: Consistent causal recovery for highly-available streaming dataflows
Stream processing lies in the backbone of modern businesses, being employed for mission
critical applications such as real-time fraud detection, car-trip fare calculations, traffic …
critical applications such as real-time fraud detection, car-trip fare calculations, traffic …