Finding Frequent Items in Probabilistic Data
Summary: Possible-world semantics define likely frequent items in probabilistic data, capturing structure beyond simple expected frequency. Exact offline algorithms (quadratic/cubic) and sublinear-memory streaming sampling with provable accuracy and confidence-based ranking, validated on real and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
BibTeX Citation
@inproceedings{zhang_sigmod08,
title = {{Finding Frequent Items in Probabilistic Data}},
author = {Zhang, Qin and Li, Feifei and Yi, Ke},
series = {{SIGMOD} '08},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1376616.1376698},
url = {https://dl.acm.org/doi/10.1145/1376616.1376698},
year = {2008}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,326 | Sliding-Window Top-k Queries on Uncertain Streams | 2008 | VLDB | 7.4227632e-05 |
| 7,810 | Mining Frequent Itemsets over Uncertain Databases | 2012 | VLDB | 5.4488716e-05 |
| 10,373 | Frequency Moments in Noisy Streaming and Distributed Data under Mismatch Ambiguity | 2026 | PODS | 4.9793485e-05 |
| 12,442 | Beyond Itemsets: Mining Frequent Featuresets over Structured Items | 2015 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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