NebulaStream: An Extensible, High-Performance Streaming Engine for Multi-Modal Edge Applications
Summary: Open-source, extensible streaming engine for distributed, heterogeneous IoT data across cloud–edge. Showcases high-performance execution on low-end devices and easy customization for multi-modal, multi-frequency streams, with a real smart-ICU health-use case. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Christoph Falkensteiner (Charité - University Medicine Berlin)
- 2. Kyle Krüger (Charité - University Medicine Berlin)
- 3. Alexander Meyer (Charité - University Medicine Berlin)
- 4. Tobias Röschl (Charité - University Medicine Berlin)
- 5. Svea Wilkending (Charité - University Medicine Berlin)
- 6. Adrian Michalke (Berlin Institute for the Foundations of Learning and Data)
- 7. Aljoscha P Lepping (Berlin Institute for the Foundations of Learning and Data)
- 8. Volker Markl (Berlin Institute for the Foundations of Learning and Data)
- 9. Ricardo Martinez (Berlin Institute for the Foundations of Learning and Data)
- 10. Nils L Schubert (Berlin Institute for the Foundations of Learning and Data)
- 11. Lukas Schwerdtfeger (Berlin Institute for the Foundations of Learning and Data)
- 12. Taha Tekdogan (Berlin Institute for the Foundations of Learning and Data)
- 13. Steffen Zeuch (Berlin Institute for the Foundations of Learning and Data)
- 14. Ariane Ziehn (Berlin Institute for the Foundations of Learning and Data)
BibTeX Citation
@inproceedings{falkensteiner_sigmod25,
title = {{NebulaStream: An Extensible, High-Performance Streaming Engine for Multi-Modal Edge Applications}},
author = {Falkensteiner, Christoph and Krüger, Kyle and Meyer, Alexander and Röschl, Tobias and Wilkending, Svea and Michalke, Adrian and Lepping, Aljoscha P and Markl, Volker and Martinez, Ricardo and Schubert, Nils L and Schwerdtfeger, Lukas and Tekdogan, Taha and Zeuch, Steffen and Ziehn, Ariane},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725118},
url = {https://dl.acm.org/doi/10.1145/3722212.3725118},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,551 | APEROL: Adaptive Parallel Edge-to-cloud Runtime Optimization for Layered Workflow Execution | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,851 | Analyzing Efficient Stream Processing on Modern Hardware | 2019 | VLDB | 7.0735328e-05 |
| 5,215 | The NebulaStream Platform: Data and Application Management for the Internet of Things | 2020 | CIDR | 6.3124972e-05 |
| 10,072 | Query Compilation Without Regrets | 2024 | SIGMOD | 5.1624689e-05 |
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