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Compressing Large Boolean Matrices Using Reordering Techniques

Summary: Losslessly compresses large Boolean matrices by reordering columns to minimize Hamming-distance transitions, reducing storage and access costs. Scalable partitioning and sampling adaptations of TSP heuristics handle high-dimensional instances beyond in-memory solvers. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h433e52159f555149
Venue
VLDB
Year
2004
Pagerank
6.8721548e-05
Overall Rank
3,987 | 73.20%
DOI
10.1016/B978-012088469-8.50005-X

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{johnson_vldb04,
        title = {{Compressing Large Boolean Matrices Using Reordering Techniques}},
        author = {Johnson, David and Krishnan, Shankar and Kumar, Subodh and Chhugani, Jatin and Venkatasubramanian, Suresh},
        journal = {PVLDB},
        series = {{VLDB} '04},
        pages = {13},
        doi = {10.1016/B978-012088469-8.50005-X},
        url = {https://doi.org/10.1016/B978-012088469-8.50005-X},
        year = {2004}
}

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

Rank Cited Paper Year Venue Pagerank
32 BIRCH: An Efficient Data Clustering Method for Very Large Databases 1996 SIGMOD 0.00049737458
1,896 Performance Measurements of Compressed Bitmap Indices 1999 VLDB 9.4099587e-05
6,842 Walking Through A Very Large Virtual Environment In Real-time 2001 VLDB 5.6665639e-05
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