, Volume 15, Issue 3, pp 497–540 | Cite as

Spatiotemporal pattern queries

  • Mahmoud Attia Sakr
  • Ralf Hartmut Güting


This paper presents a novel approach to express and evaluate the complex class of queries in moving object databases called spatiotemporal pattern queries (STP queries). That is, one can specify temporal order constraints on the fulfillment of several predicates. This is in contrast to a standard spatiotemporal query that is composed of a single predicate. We propose a language design for spatiotemporal pattern queries in the context of spatiotemporal DBMSs. The design builds on the well established concept of lifted predicates. Hence, unlike previous approaches, patterns are neither restricted to specific sets of predicates, nor to specific moving object types. The proposed language can express arbitrarily complex patterns that involve various types of spatiotemporal operations such as range, metric, topological, set operations, aggregations, distance, direction, and boolean operations. This work covers the language integration in SQL, the evaluation of the queries, and the integration with the query optimizer. We also propose a simple language for defining the temporal constraints. The approach allows for queries that were never available. We provide a complete implementation in C+ + and Prolog in the context of the Secondo platform. The implementation is made publicly available online as a Secondo Plugin, which also includes automatic scripts for repeating the experiments in this paper.


Moving objects databases Trajectory Lifted predicate Spatiotemporal patterns Secondo 


  1. 1.
    Allen JF (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843CrossRefGoogle Scholar
  2. 2.
    Alvares LO, Bogorny V, Kuijpers B, Fernandes de Macedo JA, Moelans B, Vaisman A (2007) A model for enriching trajectories with semantic geographical information. In: GIS ’07: proceedings of the 15th annual ACM international symposium on advances in geographic information systems, pp 1–8Google Scholar
  3. 3.
    Bessiere C (2006) Handbook of constraint programming, chap 3. ElsevierGoogle Scholar
  4. 4.
    Cotelo Lema JA, Forlizzi L, Güting RH, Nardelli E, Schneider M (2003) Algorithms for moving objects databases. Comput J 46(6):680–712CrossRefGoogle Scholar
  5. 5.
    du Mouza C, Rigaux P (2005) Mobility patterns. Geoinformatica 9(4):297–319CrossRefGoogle Scholar
  6. 6.
    Düntgen C, Behr T, Güting RH (2009) BerlinMOD: a benchmark for moving object databases. VLDB J 18(6):1335–1368CrossRefGoogle Scholar
  7. 7.
    Erwig M, Schneider M (1999) Developments in spatio-temporal query languages. In DEXA ’99: Proceedings of the 10th international workshop on database & expert systems applications, p 441Google Scholar
  8. 8.
    Erwig Ma, Schneider M (2002) Spatio-temporal predicates. IEEE Trans Knowl Data Eng 14(4):881–901CrossRefGoogle Scholar
  9. 9.
    Erwig M (2004) Toward spatiotemporal patterns. In: de Caluwa R, de Tré G, Boudogua G (eds) Spatio-temporal databases. Springer-Verlag, New York, pp 29–54Google Scholar
  10. 10.
    Forlizzi L, Güting RH, Nardelli E, Schneider M (2000) A data model and data structures for moving objects databases. In: SIGMOD ’00: proceedings of the 2000 ACM SIGMOD international conference on management of data, pp 319–330Google Scholar
  11. 11.
    Frentzos E, Gratsias K, Pelekis N, Theodoridis Y (2007) Algorithms for nearest neighbor search on moving object trajectories. Geoinformatica 11(2):159–193CrossRefGoogle Scholar
  12. 12.
    Gudmundsson J, van Kreveld M, Speckmann B (2004) Efficient detection of motion patterns in spatio-temporal data sets. In: GIS ’04: proceedings of the 12th annual ACM international workshop on geographic information systems, pp 250–257Google Scholar
  13. 13.
    Güting RH, Almeida V, Ansorge D, Behr T, Ding Z, Höse T, Hoffmann F, Spiekermann M, Telle U (2005) Secondo: an extensible DBMS platform for research prototyping and teaching. In: ICDE ’05: proceedings of the 21st international conference on data engineering, pp 1115–1116Google Scholar
  14. 14.
    Güting RH, Behr T, Almeida V, Ding Z, Hoffmann F, Spiekermann M (2004) Secondo: an extensible DBMS architecture and prototype. Technical Report Informatik-Report 313, FernUniversität HagenGoogle Scholar
  15. 15.
    Güting RH, Behr T, Xu J (2010) Efficient k-nearest neighbor search on moving object trajectories. VLDB J (online first)Google Scholar
  16. 16.
    Güting RH, Böhlen MH, Erwig M, Jensen CS, Lorentzos NA, Schneider M, Vazirgiannis M (2000) A foundation for representing and querying moving objects. ACM Trans Database Syst 25(1):1–42CrossRefGoogle Scholar
  17. 17.
    Hadjieleftheriou M, Kollios G, Bakalov P, Tsotras VJ (2005) Complex spatio-temporal pattern queries. In: VLDB ’05: proceedings of the 31st international conference on very large data bases, pp 877–888Google Scholar
  18. 18.
    Ioannidis YE (1996) Query optimization. ACM Comput Surv 28(1):121–123CrossRefGoogle Scholar
  19. 19.
    Pelekis N, Kopanakis I, Marketos G, Ntoutsi I, Andrienko G, Theodoridis Y (2007) Similarity search in trajectory databases. In: TIME ’07: proceedings of the 14th international symposium on temporal representation and reasoning, pp 129–140Google Scholar
  20. 20.
    Schneider M (2005) Evaluation of spatio-temporal predicates on moving objects. In: ICDE ’05: proceedings of the 21st international conference on data engineering, pp 516–517Google Scholar
  21. 21.
    Wolfson O, Xu B, Chamberlain S, Jiang L (1998) Moving objects databases: issues and solutions. In: SSDBM’98: 10th international conference on scientific and statistical database management, pp 111–122Google Scholar
  22. 22.
  23. 23.
  24. 24.
  25. 25.

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Database Systems for New ApplicationsFernUniversität in HagenHagenGermany
  2. 2.Faculty of Computer and Information SciencesUniversity of Ain ShamsCairoEgypt

Personalised recommendations