, Volume 21, Issue 3, pp 429–458 | Cite as

Index-supported pattern matching on tuples of time-dependent values



Lately, the amount of mobility data recorded by GPS-enabled (and other) devices has increased drastically, entailing the necessity of efficient processing and analysis methods. In many cases, not only the geographic position, but also additional time-dependent information are traced and/or generated, according to the purpose of the evaluation. For example, in the field of animal behavior research, besides the position of the monitored animal, biologists are interested in further data like the altitude or the temperature at every measuring point. Other application domains comprise the names of streets, places of interest, or transportation modes that can be recorded along with the geographic position of a person. In this paper, we present in detail a framework for analyzing datasets with arbitrarily many time-dependent attributes. This can be considered as a major extension of our previous work, a comprehensive framework for pattern matching on symbolic trajectories with index support. For an efficient processing of different data types, a variable number of indexes of four different types that correspond to the data types of the attributes are applied. We demonstrate the expressiveness and efficiency of our approach by querying a real dataset representing taxi trips in Rome and, particularly, with a broad series of experiments using trajectories generated by BerlinMOD combined with geological raster data.


Pattern matching Tuples of time-dependent values Indexing Finite automaton 


  1. 1.
    de Almeida V T, Güting R H, Behr T (2006) Querying moving objects in Secondo. In: MDM, pp 47–51Google Scholar
  2. 2.
    Andrienko G L, Andrienko N V, Heurich M (2011) An event-based conceptual model for context-aware movement analysis. Int J Geogr Inf Sci 25(9):1347–1370CrossRefGoogle Scholar
  3. 3.
    Bayer R, McCreight E M (1972) Organization and maintenance of large ordered indices. Acta Inf 1:173–189CrossRefGoogle Scholar
  4. 4.
    Bracciale L, Bonola M, Loreti P, Bianchi G, Amici R, Rabuffi A (2014). CRAWDAD dataset roma/taxi (v. 2014-07-17). Downloaded from
  5. 5.
    Chang J W, Song M S, Um J H (2010) Tmn-tree: New trajectory index structure for moving objects in spatial networks. In: CIT, pp 1633–1638Google Scholar
  6. 6.
    Comer D (1979) Ubiquitous B-Tree. ACM Comput Surv 11(2):121–137CrossRefGoogle Scholar
  7. 7.
    Damiani M L, Issa H, Güting R H, Valdés F (2014) Hybrid queries over symbolic and spatial trajectories: A usage scenario. In: MDM, pp 341–344Google Scholar
  8. 8.
    Damiani M L, Issa H, Güting R H, Valdés F (2015) Symbolic trajectories and application challenges. SIGSPATIAL Special 7(1):51–58CrossRefGoogle Scholar
  9. 9.
    De La Briandais R (1959) File searching using variable length keys. IRE-AIEE-ACM (Western):295–298Google Scholar
  10. 10.
    Düntgen C, Behr T, Güting R H (2009) BerlinMOD: a benchmark for moving object databases. VLDB J 18(6):1335–1368CrossRefGoogle Scholar
  11. 11.
    Erwig M, Güting R H, Schneider M, Vazirgiannis M (1999) Spatio-temporal data types: an approach to modeling and querying moving objects in databases. GeoInformatica 3(3):269–296CrossRefGoogle Scholar
  12. 12.
    Forlizzi L, Güting R H, Nardelli E, Schneider M (2000) A data model and data structures for moving objects databases. In: ACM SIGMOD, pp 319–330Google Scholar
  13. 13.
    Güting R H, Behr T, Düntgen C (2010) Secondo: a platform for moving objects database research and for publishing and integrating research implementations. IEEE Data Eng Bull 33(2):56–63Google Scholar
  14. 14.
    Güting R H, Böhlen M H, Erwig M, Jensen C S, Lorentzos N A, Schneider M, Vazirgiannis M (2000) A foundation for representing and querying moving objects. ACM TODS 25(1):1–42CrossRefGoogle Scholar
  15. 15.
    Güting R H, Schneider M (2005) Moving objects databases morgan kaufmannGoogle Scholar
  16. 16.
    Güting R H, Valdés F, Damiani M L (2015) Symbolic trajectories. ACM TSAS 1(2):7:1–7:51Google Scholar
  17. 17.
    Guttman A (1984) R-trees: A dynamic index structure for spatial searching. In: SIGMOD, pp 47–57Google Scholar
  18. 18.
    Hadjieleftheriou M, Kollios G, Bakalov P, Tsotras V J (2005) Complex spatio-temporal pattern queries. In: PVLDB, pp 877–888Google Scholar
  19. 19.
    Hopcroft J E, Motwani R, Ullman J D (2001) Introduction to automata theory, languages, and computation - (2. ed.). Addison-wesley series in computer science Addison-Wesley-LongmanGoogle Scholar
  20. 20.
    Issa H, Damiani M L (2016) Efficient access to temporally overlaying spatial and textual trajectories. In: IEEE MDM, pp 262–271Google Scholar
  21. 21.
  22. 22.
    du Mouza C, Rigaux P (2004) Multi-scale classification of moving objects trajectories. In: Proceedings on SSDBM, pp 307–316Google Scholar
  23. 23.
    du Mouza C, Rigaux P (2005) Mobility patterns. GeoInformatica 9(4):297–319CrossRefGoogle Scholar
  24. 24.
    Navarro G, Raffinot M (2002) Flexible pattern matching in strings - practical on-line search algorithms for texts and biological sequences, Cambridge University PressGoogle Scholar
  25. 25.
    Newson P, Krumm J (2009) Hidden markov map matching through noise and sparseness. In: ACM SIGSPATIAL. ACM, pp 336–343Google Scholar
  26. 26.
    Nguyen-Dinh L, Aref W G, Mokbel M F (2010) Spatio-temporal access methods: Part 2 (2003 - 2010). IEEE Data Eng Bull 33(2):46–55Google Scholar
  27. 27.
    (2016). OpenStreetMap:
  28. 28.
    Parent C, Spaccapietra S, Renso C, Andrienko G L, Andrienko N V, Bogorny V, Damiani M L, Gkoulalas-Divanis A, de Macêdo J A F, Pelekis N, Theodoridis Y, Yan Z (2013) Semantic trajectories modeling and analysis. ACM Comput Surv 45(4):42Google Scholar
  29. 29.
    Pelekis N, Theodoridis Y (2014) Mobility data management and exploration springerGoogle Scholar
  30. 30.
    Pfoser D, Jensen C S, Theodoridis Y (2000) Novel approaches in query processing for moving object trajectories. In: VLDB, pp 395–406Google Scholar
  31. 31.
    Quddus M A, Ochieng W Y, Noland R B (2007) Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies 15(5):312–328CrossRefGoogle Scholar
  32. 32.
  33. 33.
    Spaccapietra S, Parent C, Damiani M L, de Macêdo J A F, Porto F, Vangenot C (2008) A conceptual view on trajectories. Data Knowl Eng 65(1):126–146Google Scholar
  34. 34.
    (2016). U.S. Geological Survey:
  35. 35.
    Valdés F, Damiani M L, Güting R H (2013) Symbolic trajectories in Secondo: Pattern matching and rewriting. In: DASFAA, pp 450–453Google Scholar
  36. 36.
    Valdés F, Güting R H (2014) Index-supported pattern matching on symbolic trajectories. In: ACM SIGSPATIAL, pp 53–62Google Scholar
  37. 37.
    Valdés F, Güting R H, Ossi F (2016) Efficient trajectory analysis for several time-dependent attributes: A case study for roe deer. In: IEEE MDM, pp 337–340Google Scholar
  38. 38.
    Vazirgiannis M, Theodoridis Y, Sellis T K (1998) Spatio-temporal composition and indexing for large multimedia applications. Multimedia Syst 6(4):284–298CrossRefGoogle Scholar
  39. 39.
    Vieira M R, Bakalov P, Tsotras V J (2010) Querying trajectories using flexible patterns Proceedings of the EDBT, pp 406–417Google Scholar
  40. 40.
    Vieira M R, Bakalov P, Tsotras V J (2011) Flextrack: a system for querying flexible patterns in trajectory databases. In: SSTD, pp 475–480Google Scholar
  41. 41.
    Vlachos M, Gunopulos D, Kollios G (2002) Discovering similar multidimensional trajectories. In: ICDE, pp 673–684Google Scholar
  42. 42.
    Yan Z, Chakraborty D, Parent C, Spaccapietra S, Aberer K (2013) Semantic trajectories: Mobility data computation and annotation. ACM TIST 4(3):49Google Scholar
  43. 43.
    Zhang C, Han J, Shou L, Lu J, La Porta T F (2014) Splitter: Mining fine-grained sequential patterns in semantic trajectories. PVLDB 7(9):769–780Google Scholar
  44. 44.
    Zheng K, Shang S, Yuan N J, Yang Y (2013) Towards efficient search for activity trajectories. In: ICDE, pp 230–241Google Scholar
  45. 45.
    Zheng Y, Zhou X (eds) (2011) Computing with Spatial Trajectories. SpringerGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Database Systems for New ApplicationsFernuniversität in HagenHagenGermany

Personalised recommendations