Temporal Join Processing

Temporal Join Processing is a computational method that enables the combination of temporally annotated relations by aligning their time-varying data according to specified time intervals or timestamps, distinguishing itself from conventional join operations through its explicit handling of temporal validity and time-based predicate evaluation.

29

Publications

1.1K

Citations

76

Authors

35

Institutions

Publications per year

2017–2026

4

Authors

76

  1. AD
    Anton Dignös

    Free University of Bozen-Bolzano, Italy

    3 publications · 123 citations

    3

    h-index

  2. TY
    T. Y. Cliff Leung

    IBM (United States), United States

    3 publications · 93 citations

    3

    h-index

  3. RR
    Richard R. Muntz

    University of California, Los Angeles, United States

    3 publications · 93 citations

    3

    h-index

  4. MK
    Martin Kaufmann

    Queen's University, Canada

    2 publications · 94 citations

    2

    h-index

  5. BG
    Bryan G. Marsh

    Environmental Systems Research Institute (United States), United States

    2 publications · 61 citations

    2

    h-index

1–5 of 76

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 34 citations

    2

    h-index

  2. Very Large Data Bases

    Conference · 2 publications · 187 citations

    2

    h-index

  3. ACM Transactions on Database Systems

    TODS · 1 publications · 68 citations

    1

    h-index

  4. The VLDB Journal

    1 publications · 20 citations

    1

    h-index

  5. 1

    h-index

1–5