Causality Tracking

Causality Tracking is a methodological framework in distributed systems that captures and enforces the partial ordering of events based on their causal dependencies, distinguishing it from mere timestamp-based synchronization by explicitly representing precedence relationships induced by message passing and shared state.

23

Publications

904

Citations

86

Authors

46

Institutions

Publications per year

2017–2026

5

Authors

86

  1. JR
    Junghwan Rhee

    Purdue University West Lafayette, United States

    2 publications · 209 citations

    2

    h-index

  2. AV
    Anh Vo

    University of Utah, United States

    2 publications · 35 citations

    2

    h-index

  3. NP
    Nuno Preguiça

    Universidade Nova de Lisboa, Portugal

    2 publications · 24 citations

    2

    h-index

  4. MR
    Michel Raynal

    Institut de Recherche en Informatique et Systèmes Aléatoires, France

    2 publications · 28 citations

    2

    h-index

  5. KJ
    Kangkook Jee

    Princeton University, United States

    2 publications · 209 citations

    2

    h-index

1–5 of 86

Venues

  1. Annual Computer Security Applications Conference

    Conference · 1 publications · 32 citations

    1

    h-index

  2. Environmental Science and Pollution Research

    ESPR · 1 publications · 161 citations

    1

    h-index

  3. OpenGrey (Institut de l'Information Scientifique et Technique)

    Repository · 1 publications · 16 citations

    1

    h-index

  4. Purdue e-Pubs (Purdue University System)

    Repository · 1 publications · 18 citations

    1

    h-index

  5. Information Processing Letters

    1 publications · 37 citations

    1

    h-index

1–5