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Mining spatiotemporally invariant patterns

Published:22 November 2022Publication History

ABSTRACT

Discovering patterns that represent key spatial or temporal dependencies among data is a well-known exploratory data mining task. However, prior works either separately analyze spatial and temporal dependencies or discover joint spatiotemporal properties of specific trajectories observed over a region of interest. With the goal of generalizing the information provided by spatiotemporal patterns, in this paper we extract sequences of discrete events showing spatiotemporally invariant properties. We seek patterns whose corresponding instances in the source data differ only due to an invariant spatiotemporal transformation. We denote such a new type of patterns as SpatioTemporally Invariant. We also propose an efficient algorithm to mine STInvs and validate its efficiency and effectiveness on real data.

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      • Published in

        cover image ACM Conferences
        SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
        November 2022
        806 pages
        ISBN:9781450395298
        DOI:10.1145/3557915

        Copyright © 2022 Owner/Author

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        Association for Computing Machinery

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        Publication History

        • Published: 22 November 2022

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