Mapreduce Algorithms

MapReduce Algorithms is a distributed programming model for processing large datasets in parallel by applying a map function to partition data and a reduce function to aggregate results, distinguished by its scalability and fault tolerance in handling batch-oriented workloads across clusters.

2.4K

Publications

180.8K

Citations

6.4K

Authors

1.6K

Institutions

Publications per year

2017–2026

633

Authors

6.4K

  1. FH
    Francisco Herrera

    Universidad de Granada, Spain

    23 publications · 2.8K citations

    20

    h-index

  2. JL
    Jimmy Lin

    University of Waterloo, Canada

    18 publications · 1.8K citations

    16

    h-index

  3. IS
    Ion Stoica

    University of California, Berkeley, United States

    15 publications · 8.4K citations

    15

    h-index

  4. JT
    Jian Tan

    IBM (United States), United States

    13 publications · 826 citations

    12

    h-index

  5. LZ
    Li Zhang

    IBM (United States), United States

    13 publications · 826 citations

    12

    h-index

1–5 of 6.4K

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 57 publications · 7.7K citations

    43

    h-index

  2. Future Generation Computer Systems

    FGCS · 36 publications · 1.7K citations

    23

    h-index

  3. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 26 publications · 1.5K citations

    22

    h-index

  4. Cluster Computing

    24 publications · 728 citations

    18

    h-index

  5. Journal Of Big Data

    28 publications · 1.3K citations

    18

    h-index

1–5