Scalable Database Systems

Scalable Database Systems is a database system design paradigm that enables linear or near-linear expansion in data processing capacity and query throughput through the addition of computational resources in a parallel architecture, distinguishing it from static or fixed-capacity database systems.

454

Publications

38.2K

Citations

1.3K

Authors

341

Institutions

Publications per year

2017–2026

77

Authors

1.3K

  1. DJ
    David J. DeWitt

    University of Wisconsin–Madison, United States

    18 publications · 2.2K citations

    15

    h-index

  2. MS
    Michael Stonebraker

    University of California, Berkeley, United States

    15 publications · 3.3K citations

    14

    h-index

  3. MJ
    Michael J. Carey

    University of Wisconsin–Madison, United States

    10 publications · 837 citations

    10

    h-index

  4. MK
    Martin Kersten

    Centrum Wiskunde & Informatica, Netherlands

    10 publications · 1.1K citations

    10

    h-index

  5. WL
    Wolfgang Lehner

    Technische Universität Dresden, Germany

    9 publications · 655 citations

    9

    h-index

1–5 of 1.3K

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 35 publications · 3.7K citations

    26

    h-index

  2. ACM SIGMOD Record

    18 publications · 3.1K citations

    15

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 9 publications · 1.1K citations

    9

    h-index

  4. arXiv (Cornell University)

    Repository · 9 publications · 229 citations

    9

    h-index

  5. ACM Transactions on Database Systems

    TODS · 8 publications · 1.5K citations

    8

    h-index

1–5