Temporal Data Abstraction

Temporal Data Abstraction is a methodological framework that systematically reduces the complexity of time-varying datasets by identifying and representing higher-level temporal patterns, events, or states through the elimination of redundant or less significant temporal details, thereby enabling more effective analysis and visualization of dynamic phenomena.

106

Publications

8.1K

Citations

226

Authors

110

Institutions

Publications per year

2017–2026

17

Authors

226

  1. YS
    Yuval Shaḥar

    Ben-Gurion University of the Negev, Israel

    21 publications · 994 citations

    16

    h-index

  2. RB
    Riccardo Bellazzi

    University of Pavia, Italy

    9 publications · 425 citations

    9

    h-index

  3. MA
    Mark A. Musen

    Stanford University, United States

    8 publications · 440 citations

    8

    h-index

  4. LS
    Lucia Sacchi

    University of Pavia, Italy

    6 publications · 313 citations

    6

    h-index

  5. SM
    Silvia Miksch

    TU Wien, Austria

    6 publications · 473 citations

    6

    h-index

1–5 of 226

Venues

  1. PubMed

    Repository · 14 publications · 351 citations

    11

    h-index

  2. arXiv (Cornell University)

    Repository · 7 publications · 171 citations

    7

    h-index

  3. Artificial Intelligence in Medicine

    AIM · 5 publications · 554 citations

    5

    h-index

  4. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 4 publications · 543 citations

    4

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 116 citations

    3

    h-index

1–5