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Spatiotemporal Data Mining: A Survey

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arxiv 2206.12753 v1 pith:7IAVRKSL submitted 2022-06-26 cs.DB cs.CVcs.DCcs.LG

classification cs.DBcs.CVcs.DCcs.LG
keywords dataspatiotemporalminingsurveycostparallelpatternsthey
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Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science, etc. This problem is challenging because of the high societal cost of spurious patterns and exorbitant computational cost. Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.

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Cited by 1 Pith paper

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  1. Towards Physics-informed Diffusion for Anomaly Detection in Trajectories

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A diffusion model regularized with kinematic bicycle constraints detects synthetic trajectory anomalies more accurately than prior methods, but the evaluation depends on anomalies that match the physics prior.

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