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Storing Matrices on Disk: Theory and Practice Revisited

Summary: Proposes Linearized Array B-tree (LAB-tree) for on-disk matrices with flexible layouts that adapt to sparsity patterns across portions of an array and over time. Revisits B-tree insert/split strategies and flushing policies, proposing theoretically guaranteed and empirically strong alternatives for scalable matrix storage. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10171
Venue
VLDB
Year
2011
Pagerank
4.3441378e-05
Overall Rank
9,426 | 34.43%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
1,532 Data Management in Machine Learning: Challenges, Techniques, and Systems 2017 SIGMOD 0.00011472681
4,259 Optimizing I/O for Big Array Analytics 2012 VLDB 6.3147285e-05
10,378 HyperMR: Efficient Hypergraph-enhanced Matrix Storage on Compute-in-Memory Architecture 2025 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

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

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