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NetCube: A Scalable Tool for Fast Data Mining and Compression

Summary: NetCube represents DataCubes with Bayesian networks, exploiting attribute correlations to compress multidimensional counts while supporting arbitrary aggregate queries in time independent of database size. Linear-time, parallelizable construction delivers high compression (≥1800:1) and low error (<5%). (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
8946
Venue
VLDB
Year
2001
Pagerank
5.4834308e-05
Overall Rank
8,121 | 44.29%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{margaritis_vldb01,
        title = {{NetCube: A Scalable Tool for Fast Data Mining and Compression}},
        author = {Margaritis, Dimitris and Faloutsos, Christos and Thrun, Sebastian},
        journal = {PVLDB},
        series = {{VLDB} '01},
        year = {2001}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
4,205 Sample Debiasing in the Themis Open World Database System 2020 SIGMOD 6.8337021e-05
6,038 Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse 2018 VLDB 5.9990929e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

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

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