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MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions
Summary: Proposes MNC, a simple count-based matrix synopsis that exploits structural sparsity to estimate intermediates for linear algebra expressions. Sketch propagation with expression-aware estimators yields accurate sparsity at very low overhead, enabling practical plan and memory-aware ML systems.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
5728
Venue
SIGMOD
Year
2019
Pagerank
6.7178579e-05
Overall Rank
4,409 | 69.76%
DOI
10.1145/3299869.3319854
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{sommer_sigmod19,
title = {{MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions}},
author = {Sommer, Johanna and Boehm, Matthias and Evfimievski, Alexandre V. and Reinwald, Berthold and Haas, Peter J.},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3319854},
url = {https://dl.acm.org/doi/10.1145/3299869.3319854},
year = {2019}
}
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