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Measuring the Significance of Spatiotemporal Co-Occurrences

Authors:

Rafal A. Angryk,

Petrus C. MartensAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 3, Issue 3

Article No.: 9, Pages 1 - 35

https://doi.org/10.1145/3139351

Published: 02 November 2017 Publication History

Abstract

Spatiotemporal co-occurrences are the appearances of spatial and temporal overlap relationships among trajectory-based spatiotemporal instances with region-based geometric representations. Assessing the significance of spatiotemporal co-occurrences plays an important role in the spatiotemporal frequent pattern mining applications of moving region objects. A spatiotemporal version of the popular Jaccard measure has been used for measuring the strength of spatiotemporal co-occurrences. We will demonstrate the shortcomings of the Jaccard (J) measure when it is used for assessing the significance of co-occurrences among spatiotemporal instances with highly different spatiotemporal evolution characteristics. We will present two extended novel measures (J⁺ and J^*) that address the problems linked to the J measure. Our work includes algorithms for the significance measure calculations, the proofs and explanations about the key properties of measures, and a detailed experimental evaluation section. Our experiments include in-depth relevancy and running time analyses demonstrating the suitability of our proposed measures for spatiotemporal frequent pattern mining algorithms.

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Cited By

Wu YWeng DDeng ZBao JXu MWang ZZheng YDing ZChen W(2021)Towards Better Detection and Analysis of Massive Spatiotemporal Co-Occurrence PatternsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.298322622:6(3387-3402)Online publication date: Jun-2021
https://doi.org/10.1109/TITS.2020.2983226
Aydin BBoubrahimi SKucuk ANezamdoust BAngryk R(2021)Spatiotemporal event sequence discovery without thresholdsGeoinformatica10.1007/s10707-020-00427-625:1(149-177)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s10707-020-00427-6
Sharmiladevi SSiva Sathya SRamesh N(2021)A Survey on Spatiotemporal Co-occurrence Pattern Mining TechniquesApplications of Artificial Intelligence in Engineering10.1007/978-981-33-4604-8_18(225-238)Online publication date: 11-May-2021
https://doi.org/10.1007/978-981-33-4604-8_18
Show More Cited By

Index Terms

Measuring the Significance of Spatiotemporal Co-Occurrences
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Spatial and physical reasoning
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Association rules
    2. Spatial-temporal systems

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Published In

cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 3, Issue 3

September 2017

87 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/3146386

Editor:
Hanan Samet
University of Maryland, College Park

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Copyright © 2017 ACM.

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Publication History

Published: 02 November 2017

Accepted: 01 September 2017

Revised: 01 September 2017

Received: 01 April 2016

Published in TSAS Volume 3, Issue 3

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Cited By

Wu YWeng DDeng ZBao JXu MWang ZZheng YDing ZChen W(2021)Towards Better Detection and Analysis of Massive Spatiotemporal Co-Occurrence PatternsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.298322622:6(3387-3402)Online publication date: Jun-2021
https://doi.org/10.1109/TITS.2020.2983226
Aydin BBoubrahimi SKucuk ANezamdoust BAngryk R(2021)Spatiotemporal event sequence discovery without thresholdsGeoinformatica10.1007/s10707-020-00427-625:1(149-177)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s10707-020-00427-6
Sharmiladevi SSiva Sathya SRamesh N(2021)A Survey on Spatiotemporal Co-occurrence Pattern Mining TechniquesApplications of Artificial Intelligence in Engineering10.1007/978-981-33-4604-8_18(225-238)Online publication date: 11-May-2021
https://doi.org/10.1007/978-981-33-4604-8_18
Cai XAydin BGeorgoulis MAngryk R(2019)An Application of Spatio-temporal Co-occurrence Analyses for Integrating Solar Active Region Data from Multiple Reporting Modules2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006185(4950-4959)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006185
Theodoropoulos GTritsarolis ATheodoridis Y(2019)EvolvingClusters: Online Discovery of Group Patterns in Enriched Maritime DataMultiple-Aspect Analysis of Semantic Trajectories10.1007/978-3-030-38081-6_5(50-65)Online publication date: 16-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-38081-6_5
Aydin BAngryk RAydin BAngryk R(2018)Spatiotemporal Event Sequence (STES) MiningSpatiotemporal Frequent Pattern Mining from Evolving Region Trajectories10.1007/978-3-319-99873-2_6(71-96)Online publication date: 16-Oct-2018
https://doi.org/10.1007/978-3-319-99873-2_6
Aydin BAngryk RAydin BAngryk R(2018)Significance Measurements for Spatiotemporal Co-occurrencesSpatiotemporal Frequent Pattern Mining from Evolving Region Trajectories10.1007/978-3-319-99873-2_4(29-53)Online publication date: 16-Oct-2018
https://doi.org/10.1007/978-3-319-99873-2_4
Aydin BKucuk ABoubrahimi SAngryk R(2017)Top-(R%, K) Spatiotemporal Event Sequence Mining2017 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2017.39(250-257)Online publication date: Nov-2017
https://doi.org/10.1109/ICDMW.2017.39

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