Mining spatiotemporally invariant patterns
Article No.: 63, Pages 1 - 4
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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Index Terms
- Mining spatiotemporally invariant patterns
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November 2022
806 pages
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Association for Computing Machinery
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Published: 22 November 2022
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SIGSPATIAL '22
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SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems
November 1 - 4, 2022
Washington, Seattle
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Overall Acceptance Rate 220 of 1,116 submissions, 20%
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