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Multiple Aggregations Over Data Streams

Summary: Two-level LFTA/HFTA architecture enables efficient multiple aggregations on IP streams using phantom granularity for shared work. It frames phantom/query configuration as two subproblems in a cost-optimization model; hardness is proven, with greedy heuristics and experiments on real and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3706
Venue
SIGMOD
Year
2005
Pagerank
6.6012764e-05
Overall Rank
4,623 | 68.29%
DOI
10.1145/1066157.1066192

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhang_sigmod05,
        title = {{Multiple Aggregations Over Data Streams}},
        author = {Zhang, Rui and Koudas, Nick and Ooi, Beng Chin and Srivastava, Divesh},
        series = {{SIGMOD} '05},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1066157.1066192},
        url = {https://dl.acm.org/doi/10.1145/1066157.1066192},
        year = {2005}
}

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