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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)

Paper ID
9836
Venue
VLDB
Year
2007
Pagerank
5.3483178e-05
Overall Rank
8,940 | 38.67%
DOI
10.5555/1325851.1325965

Incoming Non-self Citations Over Time

Authors

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)

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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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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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