, Volume 18, Issue 4, pp 699–746 | Cite as

Group spatiotemporal pattern queries

  • Mahmoud Attia SakrEmail author
  • Ralf Hartmut Güting


Group spatiotemporal patterns are certain formations, in space and time, shown by groups of moving objects, such as flocks, concurrence, encounter, etc. A large number of recent applications focus on the collective behavior of moving objects, rather than the individual movements. Therefore finding such groups in moving object databases is crucial. There exist, in the literature, smart algorithms for matching some of these patterns. These solutions, however, address specific patterns and require specialized data representation and indexes. They share too little to be integrated into a single system. There is a need for a generic query method that allows users to fill in pattern descriptions, and retrieve the set of matches. In this paper, we propose generic query operators that can consistently express and match a wide range of group spatiotemporal patterns. We formally define these operators, illustrate the evaluation algorithms, and discuss the issues of their integration with moving object database (MOD) systems. These operators have been implemented in the context of Secondo MOD system, and the implementation is available online as open source. Several examples are given to showcase the expressive power of the operators. We have made available scripts that can be invoked from the Secondo interface to automatically repeat some of the experiments in this paper.


Collective behavior Moving object databases Secondo Spatiotemporal pattern queries 


  1. 1.
    Geopkdd website geographic privacy-aware knowledge discovery and delivery.
  2. 2.
  3. 3.
    Allen JF (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843. doi: 10.1145/182.358434 CrossRefGoogle Scholar
  4. 4.
    Andrienko G, Andrienko N, Wrobel S (2007) Visual analytics tools for analysis of movement data. SIGKDD Explor Newsl 9:38–46. doi: 10.1145/1345448.1345455 CrossRefGoogle Scholar
  5. 5.
    Asur S, Parthasarathy S, Ucar D (2009) An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans. Knowl. Discov. Data 3(4):16:1–16:36. doi: 10.1145/631162.1631164 Google Scholar
  6. 6.
    Benkert M, Gudmundsson J, Hübner F, Wolle T (2008) Reporting flock patterns. Comput Geom Theory Appl 41(3):111–125. doi: 10.1016/j.comgeo.2007.10.003 CrossRefGoogle Scholar
  7. 7.
    Bui-Xuan BM, Ferreira A, Jarry A (2003) Computing shortest, fastest, and foremost journeys in dynamic networks. Int J Found Comput Sci 14(2):267–285. doi: 10.1142/S0129054103001728. CrossRefGoogle Scholar
  8. 8.
    Cotelo Lema JA, Forlizzi L, Güting RH, Nardelli E, Schneider M (2003) Algorithms for moving objects databases. Comput J 46(6):680–712CrossRefGoogle Scholar
  9. 9.
    Dodge S, Weibel R, Lautenschütz AK (2008) Towards a taxonomy of movement patterns. Inf Vis 7(3):240–252. doi: 10.1057/palgrave.ivs.9500182 CrossRefGoogle Scholar
  10. 10.
    Düntgen C, Behr T, Güting RH (2009) Berlinmod: a benchmark for moving object databases. VLDB J 18(6):1335–1368. doi: 10.1007/s00778-009-0142-5 CrossRefGoogle Scholar
  11. 11.
    Eppstein D, Galil Z, Italiano GF (1999) Dynamic graph algorithms. In: Atallah MJ (ed) Algorithms and theory of computation handbook, chap 8. CRC Press.
  12. 12.
    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. ACM, New York, pp 319–330. doi: 10.1145/342009.335426 CrossRefGoogle Scholar
  13. 13.
    Güting RH (1993) Second-order signature: a tool for specifying data models, query processing, and optimization. SIGMOD Rec 22(2):277–286. doi: 10.1145/170036.170079 CrossRefGoogle Scholar
  14. 14.
    Giannotti F, Nanni M, Pedreschi D, Renso C, Rinzivillo S, Trasarti R (2009) Geopkdd–geographic privacy-aware knowledge discovery. In: The European future technologies conference (FET 2009)Google Scholar
  15. 15.
    Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: KDD’07, pp 330–339Google Scholar
  16. 16.
    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. ACM, New York, pp 250–257. doi: 10.1145/032222.1032259 Google Scholar
  17. 17.
    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. IEEE Computer Society, Washington, DC, pp 1115–1116Google Scholar
  18. 18.
    Güting RH, Behr T, Almeida V, Ding Z, Hoffmann F, Spiekermann M (2004) secondo: an extensible DBMS architecture and prototype. Tech Rep Informatik-Report 313 FernUniversität HagenGoogle Scholar
  19. 19.
    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):1–42. doi: 10.1145/352958.352963 CrossRefGoogle Scholar
  20. 20.
    Jeung H, Shen HT, Zhou X (2008) Convoy queries in spatio-temporal databases. In: ICDE ’08: proceedings of the 2008 IEEE 24th international conference on data engineering. IEEE Computer Society, Washington, DC, pp 1457–1459. doi: 10.1109/ICDE.2008.4497588
  21. 21.
    Kalnis P, Mamoulis N, Bakiras S (2005) On discovering moving clusters in spatio-temporal data. In: SSTD, pp 364–381Google Scholar
  22. 22.
    Kamath KY, Caverlee J (2011) Transient crowd discovery on the real-time social web. In: Proceedings of the fourth ACM international conference on Web search and data mining, WSDM ’11. ACM, New York, pp 585–594. doi: 10.1145/935826.1935909 Google Scholar
  23. 23.
    Laube P, Imfeld S, Weibel R (2005) Discovering relative motion patterns in groups of moving point objects. Int J Geogr Inf Sci 19(6):639–668CrossRefGoogle Scholar
  24. 24.
    Laube P, Kreveld M, Imfeld S (2004) Finding REMO—detecting relative motion patterns in geospatial lifelines. In: Developments in spatial data handling: proceedings of the 11th international symposium on spatial data handling. Springer, Berlin Heidelberg, pp 201–215. doi: 10.1007/b138045 Google Scholar
  25. 25.
    Li Z, Han J, Ji M, Tang LA, Yu Y, Ding B, Lee JG, Kays R (2011) Movemine: mining moving object data for discovery of animal movement patterns. ACM Trans Intell Syst Technol 2(4):37:1–37:32. doi: 10.1145/989734.1989741 CrossRefGoogle Scholar
  26. 26.
    Li Z, Ji M, Lee JG, Tang LA, Yu Y, Han J, Kays R (2010) MoveMine: mining moving object databases. In: SIGMOD ’10: proceedings of the 2010 international conference on management of data. ACM, New York, pp 1203–1206. doi: 10.1145/807167.1807319 Google Scholar
  27. 27.
    Ortale R, Ritacco E, Pelekis N, Trasarti R, Costa G, Giannotti F, Manco G, Renso C, Theodoridis Y (2008) The daedalus framework: progressive querying and mining of movement data. In: GIS, p 52Google Scholar
  28. 28.
    Pelekis N, Theodoridis Y, Vosinakis S, Panayiotopoulos T (2006) HERMES–a framework for location-based data management. In: Proceedings of EDBT 2006Google Scholar
  29. 29.
    Ramanathan A, Agarwal PK, Kurnikova M, Langmead CJ (2009) An online approach for mining collective behaviors from molecular dynamics simulations. In: Proceedings of the 13th annual international conference on research in computational molecular biology, RECOMB 2’09. Springer-Verlag, Berlin, Heidelberg, pp 138–154. doi: 10.1007/978-3-642-02008-7_10 Google Scholar
  30. 30.
    Ren C, Lo E, Kao B, Zhu X, Cheng R (2011) On querying historical evolving graph sequences. PVLDB 4(11):726–737Google Scholar
  31. 31.
    Sakr M (2012) Spatiotemporal pattern queries. Ph.D. thesis, Fern Universität Hagen.
  32. 32.
    Sakr M, Güting RH (2011) Spatiotemporal pattern queries. GeoInformatica 15:497–540. doi: 10.1007/s10707-010-0114-3 CrossRefGoogle Scholar
  33. 33.
    Tang LA, Zheng Y, Yuan J, Han J, Leung A, Hung CC, Peng WC (2012) On discovery of traveling companions from streaming trajectories. In: IEEE 28th international conference on data engineering (ICDE) 2012, pp. 186–197. doi: 10.1109/ICDE.2012.33
  34. 34.
    Trasarti R (2010) Mastering the spatio-temporal knowledge discovery process. Ph.D. thesis, University of Pisa Department of Computer Science, ItalyGoogle Scholar
  35. 35.
    Trasarti R, Giannotti F, Nanni M, Pedreschi D, Renso C (2011) A query language for mobility data mining. IJDWM 7(1):24–45Google Scholar
  36. 36.
    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
  37. 37.
    Xiao D, Eltabakh M (2013) Stepq: Spatio-temporal engine for complex pattern queries. In: Nascimento M, Sellis T, Cheng R, Sander J, Zheng Y, Kriegel HP, Renz M, Sengstock C (eds) Advances in spatial and temporal databases, lecture notes in computer science, vol 8098, pp 386–390. Springer Berlin HeidelbergGoogle Scholar
  38. 38.
    Zheng K, Zheng Y, Yuan NJ, Shang S, Zhou X (2013) Online discovery of gathering patterns over trajectories. IEEE Trans Knowl Data Eng 99(PrePrints): 1. doi: 10.1109/TKDE.2013.160 Google Scholar
  39. 39.
    Zheng Y, Yuan NJ, Zheng K, Shang S (2013) On discovery of gathering patterns from trajectories. In: Proceedings of the 2013 IEEE international conference on data engineering (ICDE 2013), ICDE ’13. IEEE Computer Society, Washington, DC, pp 242–253. doi: 10.1109/ICDE.2013.6544829
  40. 40.
    Zheng Y, Zhou X (eds) (2011) Computing with Spatial Trajectories. SpringerGoogle Scholar
  41. 41.
    Zhou S, Chen D, Cai W, Luo L, Low MYH, Tian F, Tay VSH, Ong DWS, Hamilton BD (2010) Crowd modeling and simulation technologies. ACM Trans Model Comput Simul 20(4):20:1–20:35. doi: 10.1145/842722.1842725 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

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

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