Spatiotemporal Sequence Mining

Spatiotemporal Sequence Mining is a subfield of sequence mining that focuses on discovering temporal patterns and sequential relationships in data characterized by both spatial and temporal attributes, distinguishing it from conventional sequence mining by explicitly integrating location and time as integral dimensions of the sequential structure.

44

Publications

2.1K

Citations

128

Authors

63

Institutions

Publications per year

2017–2026

11

Authors

128

  1. RA
    Rafal A. Angryk

    Georgia State University, United States

    5 publications · 96 citations

    5

    h-index

  2. BA
    Berkay Aydin

    Georgia State University, United States

    5 publications · 96 citations

    5

    h-index

  3. NM
    Nikos Mamoulis

    University of Hong Kong, Hong Kong SAR China

    3 publications · 494 citations

    3

    h-index

  4. HC
    Huiping Cao

    New Mexico State University, United States

    3 publications · 494 citations

    3

    h-index

  5. DW
    David W. Cheung

    University of Hong Kong, Hong Kong SAR China

    2 publications · 337 citations

    2

    h-index

1–5 of 128

Venues

  1. Future Generation Computer Systems

    FGCS · 2 publications · 34 citations

    2

    h-index

  2. Knowledge-Based Systems

    2 publications · 67 citations

    2

    h-index

  3. Spatial Cognition and Computation

    1 publications · 11 citations

    1

    h-index

  4. Intelligent Data Analysis

    IDA · 1 publications · 11 citations

    1

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 52 citations

    1

    h-index

1–5