Distributed Query Optimization

Distributed Query Optimization is a query optimization technique that determines the most efficient execution plan for queries accessing data across multiple geographically dispersed nodes in a distributed database system, distinguishing itself by accounting for inter-node communication costs, data fragmentation, and network latency in addition to local processing costs.

185

Publications

10.2K

Citations

484

Authors

179

Institutions

Publications per year

2017–2026

16

Authors

484

  1. DB
    David Brill

    Federal Aviation Administration, United States

    6 publications · 342 citations

    6

    h-index

  2. MJ
    Michael J. Franklin

    University of California, Berkeley, United States

    6 publications · 3.2K citations

    6

    h-index

  3. AR
    Alan R. Hevner

    University of South Florida, United States

    6 publications · 352 citations

    6

    h-index

  4. SB
    S. Bing Yao

    Purdue University West Lafayette, United States

    5 publications · 351 citations

    5

    h-index

  5. AV
    Akrivi Vlachou

    University of Piraeus, Greece

    4 publications · 182 citations

    4

    h-index

1–5 of 484

Venues

  1. ACM Transactions on Database Systems

    TODS · 7 publications · 2.6K citations

    7

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 4 publications · 258 citations

    4

    h-index

  3. IEEE Transactions on Software Engineering

    TSE · 4 publications · 372 citations

    4

    h-index

  4. Proceedings of the IEEE

    3 publications · 200 citations

    3

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 163 citations

    3

    h-index

1–5