In-memory Distributed Computation

In-Memory Distributed Computation is a distributed data processing paradigm in which computational tasks are executed across a cluster of interconnected nodes that primarily store and manipulate data in main memory, thereby distinguishing itself from disk-based approaches through its emphasis on low-latency access and high-throughput processing of large-scale datasets.

53

Publications

6.4K

Citations

231

Authors

83

Institutions

Publications per year

2017–2026

21

Authors

231

  1. MJ
    Michael J. Franklin

    University of California, Berkeley, United States

    4 publications · 4.1K citations

    4

    h-index

  2. IS
    Ion Stoica

    University of California, Berkeley, United States

    3 publications · 4.1K citations

    3

    h-index

  3. SS
    Scott Shenker

    University of California, Berkeley, United States

    3 publications · 4.1K citations

    3

    h-index

  4. MZ
    Matei Zaharia

    Stanford University, United States

    3 publications · 4.1K citations

    3

    h-index

  5. PY
    Paraskevas Yiapanis

    University of Manchester, United Kingdom

    2 publications · 76 citations

    2

    h-index

1–5 of 231

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 3 publications · 248 citations

    3

    h-index

  2. Operating Systems Design and Implementation

    Conference · 1 publications · 224 citations

    1

    h-index

  3. 1

    h-index

  4. Computer

    1 publications · 21 citations

    1

    h-index

  5. Journal of Parallel and Distributed Computing

    1 publications · 19 citations

    1

    h-index

1–5