Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Spatiotemporal Data Mining

  • Nikos Mamoulis
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_361

Synonyms

Data mining in moving object databases

Definition

The extraction of implicit, nontrivial, and potentially useful abstract information from large collections of spatio-temporal data are referred to as spatio-temporal data mining. There are two classes of spatio-temporal databases. The first category includes timestamped sequences of measurements generated by sensors distributed in a map and temporal evolutions of thematic maps (e.g., weather maps). The second class is moving object databases that consist of object trajectories (e.g., movements of cars in a city). A trajectory can be modeled as a sequence of (pi, ti) pairs, where pi corresponds to a spatial location and tiis a timestamp. The management and analysis of spatio-temporal data has gained interest recently, mainly due to the rapid advancements in telecommunications (e.g., GPS, cellular networks, etc.), which facilitate the collection of large datasets of object locations (e.g., cars, mobile phone users) and...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Berndt D, Clifford J. Using dynamic time warping to find patterns in time series. In: Proceedings of the AAAI-94 Workshop on Knowledge Discovery in Databases; 1994.Google Scholar
  2. 2.
    Cao H, Mamoulis N, Cheung DW. Mining frequent spatio-temporal sequential patterns. In: Proceedings of the 5th IEEE International Conference on Data Mining; 2005. p. 82–89.Google Scholar
  3. 3.
    Das G, Gunopulos D, Mannila H. Finding similar time series. In: Advances in Knowledge Discovery and Data Mining, 1st Pacific-Asia Conference; 1997. p. 88–100.Google Scholar
  4. 4.
    Gaffney S, Smyth P. Trajectory clustering with mixtures of regression models. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 1999. p. 63–72.Google Scholar
  5. 5.
    Hadjieleftheriou M, Kollios G, Gunopulos D, Tsotras VJ. On-line discovery of dense areas in spatio-temporal databases. In: Proceedings of the 8th International Symposium on Advances in Spatial and Temporal Databases; 2003. p. 306–324.CrossRefGoogle Scholar
  6. 6.
    Han J, Kamber M. Data mining: concepts and techniques. San Francisco: Morgan Kaufmann; 2000.zbMATHGoogle Scholar
  7. 7.
    Kalnis P, Mamoulis N, Bakiras S. On discovering moving clusters in spatio-temporal data. In: Proceedings of the 9th International Symposium on Advances in Spatial and Temporal Databases; 2005. p. 364–381.CrossRefGoogle Scholar
  8. 8.
    Lee J-G, Han J, Whang K-Y. Trajectory clustering: a partition-and-group framework. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2007. p. 593–604.Google Scholar
  9. 9.
    Mamoulis N, Cao H, Kollios G, Hadjieleftheriou M, Tao Y, Cheung D.W. Mining, indexing, and querying historical spatio-temporal data. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2004. p. 236–245.Google Scholar
  10. 10.
    Tao Y, Faloutsos C, Papadias D, Liu B. Prediction and indexing of moving objects with unknown motion patterns. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2004. p. 611–622.Google Scholar
  11. 11.
    Tsoukatos I, Gunopulos D. Efficient mining of spatio-temporal patterns. In: Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases; 2001. p. 425–442.CrossRefGoogle Scholar
  12. 12.
    Vlachos M, Gunopulos D, Kollios G. Discovering similar multidimensional trajectories. In: Proceedings of the 18th International Conference on Data Engineering; 2002. p. 673–684.Google Scholar
  13. 13.
    Zaki MJ. Spade: an efficient algorithm for mining frequent sequences. Mach Learn. 2001;42(1/2):31–60.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Hong KongHong KongChina

Section editors and affiliations

  • Dimitris Papadias
    • 1
  1. 1.Dept. of Computer Science and Eng.Hong Kong Univ. of Science and TechnologyKowloonHong Kong SAR