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Compressing Large Boolean Matrices Using Reordering Techniques

Summary: Lossless compression of large boolean matrices by reordering columns treated as points in high-dimensional Hamming space, reduced to a TSP instance. An instance-partitioning and sampling strategy adapts TSP heuristics for scalability, yielding faster disk access and improved compression on visualization and graph/mining workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9180
Venue
VLDB
Year
2004
Pagerank
6.6328898e-05
Overall Rank
3,916 | 72.76%
DOI
-

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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
33 BIRCH: An Efficient Data Clustering Method for Very Large Databases 1996 SIGMOD 0.00077324389
1,951 Performance Measurements of Compressed Bitmap Indices 1999 VLDB 9.9685919e-05
5,853 Walking Through A Very Large Virtual Environment In Real-time 2001 VLDB 5.3006479e-05
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