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Continuous Matrix Approximation on Distributed Data

Summary: Distributed streaming protocols continuously maintain a compact matrix B approximating A in every direction, with additive ε‖A‖F² error. A deterministic scheme achieves O((m/ε) log(βN)) communication, validated on large real datasets. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11145
Venue
VLDB
Year
2014
Pagerank
5.7985588e-05
Overall Rank
6,697 | 54.06%
DOI
10.14778/2732951.2732954

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{ghashami_vldb14,
        title = {{Continuous Matrix Approximation on Distributed Data}},
        author = {Ghashami, Mina and Phillips, Jeff M. and Li, Feifei},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {10},
        pages = {809--820},
        doi = {10.14778/2732951.2732954},
        url = {https://doi.org/10.14778/2732951.2732954},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
6,696 Matrix Sketching Over Sliding Windows 2016 SIGMOD 5.7985588e-05
8,268 Efficient Matrix Sketching over Distributed Data 2017 PODS 5.4574671e-05
10,427 AeroSketch: Near-Optimal Time Matrix Sketch Framework for Persistent, Sliding Window, and Distributed Streams 2026 SIGMOD 5.093636e-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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