Adaptive Query Optimization

Adaptive Query Optimization is a query optimization paradigm that dynamically adjusts execution plans during query processing based on runtime information, distinguishing itself from static and dynamic optimization approaches by incorporating feedback mechanisms to respond to unforeseen variations in data characteristics and system conditions.

85

Publications

4.3K

Citations

258

Authors

100

Institutions

Publications per year

2017–2026

29

Authors

258

  1. RM
    Ryan Marcus

    Massachusetts Institute of Technology, United States

    5 publications · 325 citations

    5

    h-index

  2. TK
    Tim Kraska

    Massachusetts Institute of Technology, United States

    5 publications · 301 citations

    5

    h-index

  3. MF
    Marc Friedman

    University of Washington, United States

    4 publications · 655 citations

    4

    h-index

  4. PN
    Parimarjan Negi

    Massachusetts Institute of Technology, United States

    4 publications · 280 citations

    4

    h-index

  5. NT
    Nesime Tatbul

    Intel (United States), United States

    4 publications · 291 citations

    4

    h-index

1–5 of 258

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 9 publications · 277 citations

    9

    h-index

  2. ACM Transactions on Database Systems

    TODS · 4 publications · 335 citations

    4

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 110 citations

    4

    h-index

  4. ACM SIGMOD Record

    3 publications · 490 citations

    3

    h-index

  5. Information Systems

    2 publications · 52 citations

    2

    h-index

1–5