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Mining targeted spatio-temporal sequential patterns.

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Title: Mining targeted spatio-temporal sequential patterns.
Authors: Maciąg, Piotr S.1 (AUTHOR) piotr.maciag@pw.edu.pl
Source: GeoInformatica. Jul2025, Vol. 29 Issue 3, p435-463. 29p.
Subjects: Sequential pattern mining, Spatiotemporal processes, Algorithms, Mathematical optimization, Data analysis
Abstract: This article addresses the problem of Target-oriented Spatio-temporal Sequential Pattern (TaSTSP) mining by introducing the concept of TaSTSP and its theoretical properties. It presents the TaSTSPM algorithm, a tool designed for efficient and effective pattern mining, incorporating four pruning strategies to accelerate the mining process. The article examines the time and space complexity of the proposed algorithms. Experiments using the Boston Crime Dataset and the Seattle Collision Dataset demonstrate that TaSTSPM outperforms other state-of-the-art algorithms in identifying spatio-temporal sequential patterns. Finally, the article provides examples of useful patterns discovered from these datasets. [ABSTRACT FROM AUTHOR]
Copyright of GeoInformatica is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Mining targeted spatio-temporal sequential patterns.
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  Data: <searchLink fieldCode="AR" term="%22Maciąg%2C+Piotr+S%2E%22">Maciąg, Piotr S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> piotr.maciag@pw.edu.pl</i>
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  Data: <searchLink fieldCode="DE" term="%22Sequential+pattern+mining%22">Sequential pattern mining</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink>
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  Data: This article addresses the problem of Target-oriented Spatio-temporal Sequential Pattern (TaSTSP) mining by introducing the concept of TaSTSP and its theoretical properties. It presents the TaSTSPM algorithm, a tool designed for efficient and effective pattern mining, incorporating four pruning strategies to accelerate the mining process. The article examines the time and space complexity of the proposed algorithms. Experiments using the Boston Crime Dataset and the Seattle Collision Dataset demonstrate that TaSTSPM outperforms other state-of-the-art algorithms in identifying spatio-temporal sequential patterns. Finally, the article provides examples of useful patterns discovered from these datasets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of GeoInformatica is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10707-025-00535-1
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Spatiotemporal processes
        Type: general
      – SubjectFull: Algorithms
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      – SubjectFull: Mathematical optimization
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              Text: Jul2025
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