Stream Processing Scalability

Stream Processing Scalability is a system-level performance characteristic that denotes the capacity of a stream processing architecture to maintain bounded latency and throughput as input data rates increase, distinguished by its focus on real-time data flow handling under dynamic and large-scale workloads.

141

Publications

11.6K

Citations

428

Authors

159

Institutions

Publications per year

2017–2026

51

Authors

428

  1. VG
    Vincenzo Gulisano

    Chalmers University of Technology, Sweden

    5 publications · 475 citations

    5

    h-index

  2. AK
    Asterios Katsifodimos

    Delft University of Technology, Netherlands

    5 publications · 140 citations

    5

    h-index

  3. VK
    Vana Kalogeraki

    Athens University of Economics and Business, Greece

    5 publications · 153 citations

    5

    h-index

  4. KR
    Karthik Ramasamy

    Twitter (United States), United States

    4 publications · 1.6K citations

    4

    h-index

  5. VM
    Volker Markl

    Technische Universität Berlin, Germany

    4 publications · 191 citations

    4

    h-index

1–5 of 428

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 8 publications · 418 citations

    8

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 4 publications · 656 citations

    4

    h-index

  3. IEEE Access

    2 publications · 38 citations

    2

    h-index

  4. Future Generation Computer Systems

    FGCS · 2 publications · 62 citations

    2

    h-index

  5. IEEE Internet Computing

    2 publications · 73 citations

    2

    h-index

1–5