Sublinear Space Algorithms

Sublinear Space Algorithms is a class of algorithms in streaming computation that belongs to the broader category of space-bounded algorithms, distinguished by its requirement of using memory space that is asymptotically smaller than the input size, typically o(n), to process massive data streams efficiently.

52

Publications

1.9K

Citations

100

Authors

58

Institutions

Publications per year

2017–2026

14

Authors

100

  1. SG
    Sudipto Guha

    University of Pennsylvania, United States

    4 publications · 196 citations

    4

    h-index

  2. AM
    Andrew McGregor

    University of Massachusetts Amherst, United States

    4 publications · 192 citations

    4

    h-index

  3. DP
    David P. Woodruff

    IBM Research - Almaden, United States

    4 publications · 101 citations

    4

    h-index

  4. RJ
    Rajesh Jayaram

    Carnegie Mellon University, United States

    3 publications · 72 citations

    3

    h-index

  5. SV
    Suresh Venkatasubramanian

    University of Utah, United States

    3 publications · 174 citations

    3

    h-index

1–5 of 100

Venues

  1. Theoretical Computer Science

    3 publications · 107 citations

    3

    h-index

  2. Information Processing Letters

    3 publications · 54 citations

    3

    h-index

  3. ACM Transactions on Algorithms

    TALG · 2 publications · 50 citations

    2

    h-index

  4. 2

    h-index

  5. SIAM Journal on Computing

    2 publications · 61 citations

    2

    h-index

1–5