Online Data Summarization

Online Data Summarization is a streaming algorithmic framework that continuously condenses high-volume, time-ordered data streams into compact, interpretable summaries by incrementally updating summary structures under strict space and time constraints.

78

Publications

5.1K

Citations

232

Authors

110

Institutions

Publications per year

2017–2026

24

Variants

Streaming Data Summarization

Authors

232

  1. JK
    Jugal Kalita

    University of Colorado Colorado Springs, United States

    3 publications · 395 citations

    3

    h-index

  2. AK
    Amin Karbasi

    Yale University, United States

    3 publications · 368 citations

    3

    h-index

  3. AQ
    Aziz Qaroush

    Birzeit University, Palestinian Territories

    2 publications · 121 citations

    2

    h-index

  4. BS
    Beaux Sharifi

    University of Colorado Colorado Springs, United States

    2 publications · 214 citations

    2

    h-index

  5. DS
    Damiano Spina

    RMIT University, Australia

    2 publications · 92 citations

    2

    h-index

1–5 of 232

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 355 citations

    6

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 40 citations

    3

    h-index

  3. IEEE Access

    2 publications · 78 citations

    2

    h-index

  4. ACM Transactions on Knowledge Discovery from Data

    TKDD · 2 publications · 104 citations

    2

    h-index

  5. World Wide Web

    2 publications · 55 citations

    2

    h-index

1–5