Concepedia

Ingestion Throughput Optimization

Ingestion Throughput Optimization is a methodological framework within data engineering that systematically enhances the rate and efficiency of data ingestion processes by minimizing latency, maximizing data flow velocity, and dynamically allocating system resources under varying workload conditions.

2

Publications

48

Citations

9

Authors

5

Institutions

Publications per year

2017–2026

2

Parents

Publications

2

1–2 of 2

Authors

9

  1. LW
    Li Wang

    Tianjin University, China

    1 publications · 15 citations

    1

    h-index

  2. ZL
    Zijie Lu

    Guangdong University of Technology, China

    1 publications · 15 citations

    1

    h-index

  3. DM
    Domagoj Margan

    University of Rijeka, Croatia

    1 publications · 33 citations

    1

    h-index

  4. PF
    Per Fuchs

    Imperial College London, United Kingdom

    1 publications · 33 citations

    1

    h-index

  5. MW
    Marianne Winslett

    University of Illinois Urbana-Champaign, United States

    1 publications · 15 citations

    1

    h-index

1–5 of 9

Institutions

5

  1. Advanced Digital Sciences Center

    Singapore, Singapore

    1 h-index

    3 publications

    45 citations

  2. Imperial College London

    London, United Kingdom

    1 h-index

    1 publications

    33 citations

  3. University of Illinois Urbana-Champaign

    Urbana, United States

    1 h-index

    1 publications

    15 citations

  4. Digital Science (United States)

    Cambridge, United States

    1 h-index

    3 publications

    45 citations

  5. Guangdong University of Technology

    Guangzhou, China

    1 h-index

    2 publications

    30 citations

1–5 of 5

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 48 citations

    2

    h-index

1–1