STAR: Self-Tuning Aggregation for Scalable Monitoring
Summary: STAR self-tunes numeric precision for continuous distributed-stream aggregates, optimizing communication over hierarchical aggregation trees under a fixed error budget. It allocates error using workload update rates and variance, with cost-benefit throttling, outperforming prior schemes in monitoring experiments. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Navendu Jain (University of Texas)
- 2. Dmitry Kit (University of Texas)
- 3. Prince Mahajan (University of Texas)
- 4. Praveen Yalagandula (Hewlett Packard Enterprise; University of Texas)
- 5. Mike Dahlin (University of Texas)
- 6. Yin Zhang (University of Texas)
BibTeX Citation
@article{jain_vldb07,
title = {{STAR: Self-Tuning Aggregation for Scalable Monitoring}},
author = {Jain, Navendu and Kit, Dmitry and Mahajan, Prince and Yalagandula, Praveen and Dahlin, Mike and Zhang, Yin},
journal = {PVLDB},
series = {{VLDB} '07},
pages = {962--973},
doi = {10.5555/1325851.1325965},
url = {https://doi.org/10.5555/1325851.1325965},
year = {2007}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,415 | Processing Continuous Join Queries in Sensor Networks: a Filtering Approach | 2010 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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