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Spatiotemporal event sequence discovery without thresholds

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Abstract

Spatiotemporal event sequences (STESs) are the ordered series of event types whose instances frequently follow each other in time and are located close-by. An STES is a spatiotemporal frequent pattern type, which is discovered from moving region objects whose polygon-based locations continiously evolve over time. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. The quality of the discovered sequences is of great importance to the domain experts who use these algorithms. We introduce a novel algorithm to find the most relevant STESs without threshold values. We tested the relevance and performance of our threshold-free algorithm with a case study on solar event metadata, and compared the results with the previous STES mining algorithms.

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Acknowledgements

This project has been supported in part by funding from the Division of Advanced Cyberinfrastructure within the Directorate for Computer and Information Science and Engineering, the Division of Astronomical Sciences within the Directorate for Mathematical and Physical Sciences, and the Division of Atmospheric and Geospace Sciences within the Directorate for Geosciences, under NSF award #1443061. It was also supported in part by funding from the Heliophysics Living With a Star Science Program, under NASA award #NNX15AF39G.

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Authors and Affiliations

  1. Georgia State University, Atlanta, GA, USA

    Berkay Aydin, Soukaina Filali Boubrahimi, Ahmet Kucuk, Bita Nezamdoust & Rafal A. Angryk

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  1. Berkay Aydin

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  2. Soukaina Filali Boubrahimi

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  3. Ahmet Kucuk

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  4. Bita Nezamdoust

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  5. Rafal A. Angryk

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Correspondence toBerkay Aydin.

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Appendix: Comparisons of pi values for Rand-ESMiner and EsGrowth

Appendix: Comparisons ofpi values forRand-ESMiner andEsGrowth

1.1A.1 Size-2 STESs

Fig. 8
figure 8

Mixed plots ofpi values fromRand-ESMiner andESGrowth algorithms for top-15 length-2 STES from all the datasets. Distributions ofpi values fromRand-ESMiner are shown as boxplots, whilepi values (discovered usingESGrowth) with different thresholds are shown

1.2A.2 Size-3 STESs

Fig. 9
figure 9

Mixed plots ofpi values fromRand-ESMiner andESGrowth algorithms for top-15 length-3 STES from all the datasets. Distributions ofpi values fromRand-ESMiner are shown as boxplots, whilepi values (discovered usingESGrowth) with different thresholds are shown

1.3A.3 Size-4 STESs

Fig. 10
figure 10

Mixed plots ofpi values fromRand-ESMiner andESGrowth algorithms for top-15 length-4 STES from all the datasets. Distributions ofpi values fromRand-ESMiner are shown as boxplots, whilepi values (discovered usingESGrowth) with different thresholds are shown

1.4A.4 Number of top-15 occurrences for length-3 and length-4 STESs

Fig. 11
figure 11

Number of top-15 occurrences for length-3 STESs, discovered usingRand-ESMiner from twelve monthly datasets

Fig. 12
figure 12

Number of top-15 occurrences for length-4 STESs, discovered usingRand-ESMiner from twelve monthly datasets

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Aydin, B., Boubrahimi, S.F., Kucuk, A.et al. Spatiotemporal event sequence discovery without thresholds.Geoinformatica25, 149–177 (2021). https://doi.org/10.1007/s10707-020-00427-6

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