Discovering Tight Space-Time Sequences

  • Riccardo Campisano
  • Heraldo Borges
  • Fabio Porto
  • Fabio Perosi
  • Esther Pacitti
  • Florent Masseglia
  • Eduardo OgasawaraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11031)


The problem of discovering spatiotemporal sequential patterns affects a broad range of applications. Many initiatives find sequences constrained by space and time. This paper addresses an appealing new challenge for this domain: find tight space-time sequences, i.e., find within the same process: (i) frequent sequences constrained in space and time that may not be frequent in the entire dataset and (ii) the time interval and space range where these sequences are frequent. The discovery of such patterns along with their constraints may lead to extract valuable knowledge that can remain hidden using traditional methods since their support is extremely low over the entire dataset. We introduce a new Spatio-Temporal Sequence Miner (STSM) algorithm to discover tight space-time sequences. We evaluate STSM using a proof of concept use case. When compared with general spatial-time sequence mining algorithms (GSTSM), STSM allows for new insights by detecting maximal space-time areas where each pattern is frequent. To the best of our knowledge, this is the first solution to tackle the problem of identifying tight space-time sequences.


Spatio-temporal Sequential Patterns Frequent Sequences Extract Valuable Knowledge Sequential Pattern Mining Algorithm Mean Block Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially funded by CAPES, CNPq, FAPERJ, Inria SciDISC, and the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014–2020, under grant agreement No. 732051.


  1. 1.
    Alatrista-Salas, H., Bringay, S., Flouvat, F., Selmaoui-Folcher, N., Teisseire, M.: Spatio-sequential patterns mining: beyond the boundaries. Intell. Data Anal. 20(2), 293–316 (2016)CrossRefGoogle Scholar
  2. 2.
    Aydin, B., Angryk, R.: Spatiotemporal event sequence mining from evolving regions. In: Proceedings - International Conference on Pattern Recognition, pp. 4172–4177 (2017)Google Scholar
  3. 3.
    Batu, B., Temizel, T., Duzgun, H.: A non-parametric algorithm for discovering triggering patterns of spatio-temporal event types. IEEE Trans. Knowl. Data Eng. 29(12), 2629–2642 (2017)CrossRefGoogle Scholar
  4. 4.
    Chen, Y.L., Hu, Y.H.: Constraint-based sequential pattern mining: the consideration of recency and compactness. Decis. Support Syst. 42(2), 1203–1215 (2006)CrossRefGoogle Scholar
  5. 5.
    dgbes: Seismic Interpretation Software & Services. Technical report. (2018)
  6. 6.
    Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 330–339 (2007)Google Scholar
  7. 7.
    Huang, Y., Zhang, L., Zhang, P.: A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Trans. Knowl. Data Eng. 20(4), 433–448 (2008)CrossRefGoogle Scholar
  8. 8.
    Julea, A., Méger, N., Bolon, P., Rigotti, C., Doin, M.P., Lasserre, C., Trouve, E., Lazarescu, V.: Unsupervised spatiotemporal mining of satellite image time series using grouped frequent sequential patterns. IEEE Trans. Geosci. Remote Sens. 49(4), 1417–1430 (2011)CrossRefGoogle Scholar
  9. 9.
    Li, K., Fu, Y.: Prediction of human activity by discovering temporal sequence patterns. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1644–1657 (2014)CrossRefGoogle Scholar
  10. 10.
    Li, Y., Bailey, J., Kulik, L., Pei, J.: Mining probabilistic frequent spatio-temporal sequential patterns with gap constraints from uncertain databases. In: Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 448–457 (2013)Google Scholar
  11. 11.
    Mooney, C., Roddick, J.: Sequential pattern mining - approaches and algorithms. ACM Comput. Surv. 45(2), 19 (2013)CrossRefGoogle Scholar
  12. 12.
    Sunitha, G., Rama Mohan Reddy, A.: Mining frequent patterns from spatiotemporal data sets: a survey. J. Theor. Appl. Inf. Technol. 68(2), 265–274 (2014)Google Scholar
  13. 13.
    Tsai, C.Y., Shieh, Y.C.: A change detection method for sequential patterns. Decis. Support Syst. 46(2), 501–511 (2009)CrossRefGoogle Scholar
  14. 14.
    Tsoukatos, I.I., Gunopulos, D.: Efficient mining of spatiotemporal patterns. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 425–442. Springer, Heidelberg (2001). Scholar
  15. 15.
    Zhou, H.W.: Practical Seismic Data Analysis, 1st edn. Cambridge University Press, New York (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Riccardo Campisano
    • 1
  • Heraldo Borges
    • 1
  • Fabio Porto
    • 2
  • Fabio Perosi
    • 3
  • Esther Pacitti
    • 4
  • Florent Masseglia
    • 4
  • Eduardo Ogasawara
    • 1
    Email author
  1. 1.CEFET/RJ - Federal Center for Technological Education of Rio de JaneiroRio de JaneiroBrazil
  2. 2.LNCC - National Laboratory of Scientific ComputingPetrópolisBrazil
  3. 3.UFRJ - Federal University of Rio de JaneiroRio de JaneiroBrazil
  4. 4.Inria and University of MontpellierMontpellierFrance

Personalised recommendations