Skip to main content

Spatiotemporal Trajectories

  • Reference work entry
  • First Online:
  • 39 Accesses

Synonyms

Moving object trajectories; Spatio-temporal representation

Definition

A spatio-temporal trajectory can be straightforwardly defined as a function from the temporal I ⊆ ℝ domain to the geographical space ℝ2, i.e., the 2-dimensional plane. From an application point of view, a trajectory is the recording of an object’s motion, i.e., the recording of the positions of an object at specific timestamps.

Generally speaking, spatio-temporal trajectories can be classified into two major categories, according to the nature of the underlying spatial object: (i) objects without area represented as moving points, and (ii) objects with area, represented as moving regions; in this case the region extent may also change with time. Among the above two categories, the former has attracted the main part of the research interest, since the majority of real-world applications involving spatio-temporal trajectories consider objects represented as points, e.g., fleet management systems monitoring...

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Recommended Reading

  1. Almeida VT, Guting RH. Indexing the trajectories of moving objects in networks. GeoInformatica. 2005;9(1):33–60.

    Article  Google Scholar 

  2. Arumugam S, Jermaine C. Closest-point-of-approach join for moving object histories. In: Proceedings of the 22nd International Conference on Data Engineering; 2006. p. 86.

    Google Scholar 

  3. Bakalov P, Hadjieleftheriou M, Keogh E, Tsotras V. Efficient trajectory joins using symbolic representations. In: Proceedings of the 6th International Conference on Mobile Data Management; 2005. p. 86–93.

    Google Scholar 

  4. Brakatsoulas S, Pfoser D, Salas R, Wenk C. On map-matching vehicle tracking data. In: Proceedings of the 31st International Conference on Very Large Data Bases; 2005. p. 853–64.

    Google Scholar 

  5. Chen L, Özsu MT, Oria V. Robust and fast similarity search for moving object trajectories. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2005. p. 491–502.

    Google Scholar 

  6. Chomicki J, Revesz P. A geometric framework for specifying spatiotemporal objects. In: Proceedings of the 6th International Workshop Temporal Representation and Reasoning; 1999. p. 41–6.

    Google Scholar 

  7. Erwig M, Güting RH, Schneider M, Varzigiannis M. Spatio-temporal data types: an approach to modeling and querying moving objects in databases. GeoInformatica. 1999;3(3):265–91.

    Article  Google Scholar 

  8. Forlizzi L, Güting Nardelli E, Schneider M. A data model and data structures for moving objects databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2000. p. 319–30.

    Google Scholar 

  9. Frentzos E, Gratsias K, Pelekis N, Theodoridis Y. Algorithms for nearest neighbor search on moving object trajectories. GeoInformatica. 2007;11(2):159–93.

    Article  Google Scholar 

  10. Frentzos E, Gratsias K, Theodoridis Y. Index-based most similar trajectory search. In: Proceedings of the 23rd International Conference on Data Engineering; 2007. p. 816–25.

    Google Scholar 

  11. Guting RH, Bohlen MH, Erwig M, Jensen CS, Lorentzos NA, Schneider M, Vazirgiannis M. A foundation for representing and querying moving objects. ACM Trans Database Syst. 2000;25(1):1–42.

    Article  Google Scholar 

  12. Keogh E. Exact indexing of dynamic time warping. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 406–17.

    Chapter  Google Scholar 

  13. Meratnia N, By R. Spatiotemporal compression techniques for moving point objects. In: Advances in Database Technology, Proceedings of the 9th International Conference on Extending Database Technology; 2004. p. 765–82.

    Chapter  Google Scholar 

  14. Pfoser D, Jensen CS, Theodoridis Y. Novel approaches to the indexing of moving object trajectories. In: Proceedings of the 26th International Conference on Very Large Data Bases; 2000. p. 395–406.

    Google Scholar 

  15. Tao Y, Kollios G, Considine J, Li F, Papadias D. Spatio-temporal aggregation using sketches. In: Proceedings of the 20th International Conference on Data Engineering; 2004. p. 214–26.

    Google Scholar 

  16. Vlachos M, Kollios G, Gunopulos D. Discovering similar multidimensional trajectories. In: Proceedings of the 18th International Conference on Data Engineering; 2002. p. 673–84.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Elias Frentzos or Apostolos N. Papadopoulos .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

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

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Frentzos, E., Theodoridis, Y., Papadopoulos, A.N. (2018). Spatiotemporal Trajectories. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_364

Download citation

Publish with us

Policies and ethics