Parallel Query Optimization

Parallel Query Optimization is a query optimization technique that belongs to the class of methods designed to enhance database performance by determining the most efficient execution plan for queries processed across multiple processors or nodes, distinguished by its explicit consideration of parallelism strategies such as data partitioning, task distribution, and synchronization overhead to minimize overall query response time.

110

Publications

6.4K

Citations

317

Authors

124

Institutions

Publications per year

2017–2026

16

Authors

317

  1. PV
    Patrick Valduriez

    Institut national de recherche en sciences et technologies du numérique, France

    6 publications · 231 citations

    6

    h-index

  2. WH
    Waqar Hasan

    Hewlett-Packard (United States), United States

    5 publications · 393 citations

    5

    h-index

  3. ŠG
    Šumit Ganguly

    Indiana University Bloomington, United States

    4 publications · 279 citations

    4

    h-index

  4. TK
    Tahsin Kurç

    The Ohio State University, United States

    3 publications · 49 citations

    3

    h-index

  5. MZ
    Mohamed Zaït

    Oracle (United States), United States

    3 publications · 157 citations

    3

    h-index

1–5 of 317

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 7 publications · 343 citations

    7

    h-index

  2. ACM SIGMOD Record

    6 publications · 254 citations

    6

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 499 citations

    4

    h-index

  4. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 2 publications · 59 citations

    2

    h-index

  5. Very Large Data Bases

    Conference · 2 publications · 119 citations

    2

    h-index

1–5