, Volume 17, Issue 1, pp 125–172 | Cite as

A generic data model for moving objects

  • Jianqiu XuEmail author
  • Ralf Hartmut Güting


Moving objects databases should be able to manage trips that pass through several real world environments, e.g., road network, indoor. However, the current data models only deal with the movement in one situation and cannot represent comprehensive trips for humans who can move inside a building, walk on the pavement, drive on the road, take the public vehicles (bus or train), etc. As a result, existing queries are solely limited to one environment. In this paper, we design a data model that is able to represent moving objects in multiple environments in order to support novel queries on trips in different surroundings and various transportation modes (e.g., Car, Walk, Bus). A generic and precise location representation is proposed that can apply in all environments. The idea is to let the space for moving objects be covered by a set of so-called infrastructures each of which corresponds to an environment and defines the available places for moving objects. Then, the location is represented by referencing to the infrastructure. We formulate the concept of space and infrastructure and propose the methodology to represent moving objects in different environments with the integration of precise transportation modes. Due to different infrastructure characteristics, a set of novel data types is defined to represent infrastructure components. To efficiently support new queries, we design a group of operators to access the data. We present how such a data model is implemented in a database system and report the experimental results. The new model is designed with attention to the data models of previous work for free space and road networks to have a consistent type system and framework of operators. In this way, a powerful set of generic query operations is available for querying, together with those dealing with infrastructures and transportation modes. We demonstrate these capabilities by formulating a set of sophisticated queries across all infrastructures.


Moving objects Data model Infrastructure 


  1. 1.
  2. 2.
  3. 3.
  4. 4. (2008). Accessed 18 June 2012
  5. 5. (2010). Accessed 20 Dec 2010
  6. 6. (2011). Accessed 10 May 2011
  7. 7. (2010). Accessed 20 Jan 2010
  8. 8.
    Bauer V, Gamper J, Loperfido R, Profanter S, Putzer S, Timko I (2008) Computing isochrones in multi-modal, schedule-based transport networks. In: ACM GIS, DemoGoogle Scholar
  9. 9.
    Booth J, Sistla P, Wolfson O, Cruz IF (2009) A data model for trip planning in multimodal transportation systems. In: EDBTGoogle Scholar
  10. 10.
    Brakatsoulas S, Pfoser D, Tryfona N (2004) Modeling, storing and mining moving object databases. In: IDEASGoogle Scholar
  11. 11.
    Chen L, Özsu MT, Oria V (2005) Robust and fast similarity search for moving object trajectories. In: SIGMODGoogle Scholar
  12. 12.
    Chen Z, Shen HT, Zhou X, Zheng Y, Xie X (2010) Searching trajectories by locations—an efficiency study. In: SIGMODGoogle Scholar
  13. 13.
    Ding Z, Güting RH (2004) Managing moving objects on dynamic transportation networks. In: SSDBMGoogle Scholar
  14. 14.
    Forlizzi L, Güting RH, Nardelli E, Schneider M (2000) A data model and data structures for moving objects databases. In: SIGMOD, pp 319–330Google Scholar
  15. 15.
    González MC, Hidalgo RCA, Barabási A (2008) Understanding individual human mobility patterns. Nature 453:779–282CrossRefGoogle Scholar
  16. 16.
    Grumbach S, Rigaux P, Segoufin L (2000) Manipulating interpolated data is easier than you thought. In: VLDBGoogle Scholar
  17. 17.
    Güting RH, Schneider M (2005) Moving objects databases. Morgan Kaufmann, San MateoGoogle Scholar
  18. 18.
    Güting RH, Böhlen MH, Erwig M, Jensen CS, Lorentzos NA, Schneider M, Vazirgiannis M (2000) A foundation for representing and querying moving objects. ACM TDOS 25(1):1–42CrossRefGoogle Scholar
  19. 19.
    Güting RH, Almeida V, Ansorge D, Behr T, Ding Z, Höse T, Hoffmann F, Spiekermann M (2005) Secondo: an extensible dbms platform for research prototyping and teaching. In: ICDE, demo paperGoogle Scholar
  20. 20.
    Güting RH, de Almeida VT, Ding ZM (2006) Modeling and querying moving objects in networks. VLDB J 15(2):165–190CrossRefGoogle Scholar
  21. 21.
    Güting RH, Behr T, Xu J (2010) Efficient k-nearest neighbor search on moving object trajectories. VLDB J 19(5):687–714CrossRefGoogle Scholar
  22. 22.
    Hage C, Jensen CS, Pedersen TB, Speicys L, Timko I (2003) Integrated data management for mobile services in the real world. In: VLDBGoogle Scholar
  23. 23.
    Iwerks GS, Samet H, Smith K (2003) Continuous k-nearest neighbor queries for continuous moving points with updates. In: VLDBGoogle Scholar
  24. 24.
    Jensen CS, Kligys A, Pedersen TB, Timko I (2004) Multidimensional data modeling for location-based services. VLDB J 13:1–21CrossRefGoogle Scholar
  25. 25.
    Jensen CS, Lu H, Yang B (2009) Graph model based indoor tracking. In: MDMGoogle Scholar
  26. 26.
    Jensen CS, Lu H, Yang B (2009) Indexing the trajectories of moving objects in symbolic indoor space. In: SSTDGoogle Scholar
  27. 27.
    Jeung H, Liu Q, Shen HT, Zhou X (2008) A hybrid prediction model for moving objects. In: ICDEGoogle Scholar
  28. 28.
    Jeung H, Yiu ML, Zhou X, Jensen CS, Shen HT (2008) Discovery of convoys in trajectory databases. In: VLDBGoogle Scholar
  29. 29.
    Kuijpers B, Othman W (2007) Trajectory databases: data models, uncertainty and complete query languages. In: ICDTGoogle Scholar
  30. 30.
    Lema JA, Forlizzi L, Güting RH, Schneider M (2003) Algorithms for moving objects databases. Comput J 46(6):680–712CrossRefGoogle Scholar
  31. 31.
    Lorenz B, Ohlbach HJ, Stoffel EP (2006) A hybrid spatial model for representing indoor environments. In: W2GISGoogle Scholar
  32. 32.
    Mouratidis K, Hadjieleftheriou M, Papadias D (2005) Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: SIGMODGoogle Scholar
  33. 33.
    Mouratidis K, Yiu ML, Papadias D, Mamoulis N (2006) Continuous nearest neighbor monitoring in road networks. In: VLDBGoogle Scholar
  34. 34.
    Mouratidis K, Lin Y, Yiu ML (2010) Preference queries in large multi-cost transportation networks. In: ICDEGoogle Scholar
  35. 35.
    Mouza C, Rigaux P (2005) Mobility patterns. Geoinformatica 9(4):297–319CrossRefGoogle Scholar
  36. 36.
    Mouza C, Rigaux P, Scholl M (2005) Efficient evaluation of parameterized pattern queries. In: CIKMGoogle Scholar
  37. 37.
    Praing R, Schneider M (2007) Modeling historical and future movements of spatio-temporal objects in moving objects databases. In: CIKM, pp 183–192Google Scholar
  38. 38.
    Praing R, Schneider M (2007) A universal abstract model for future movements of moving objects. In: AGILE conf., pp 111–120Google Scholar
  39. 39.
    Reddy S, Mun M, Burke J, Estrin D, Hansen MH, Srivastava MB (2010) Using mobile phones to determine transportation modes. TOSN 6(2):82–108CrossRefGoogle Scholar
  40. 40.
    Scarponcini P (2002) Generalized model for linear referencing in transportation. Geoinformatica 6(1):35–55CrossRefGoogle Scholar
  41. 41.
    Shekhar S, Coyle M, Goyal B, Liu DR, Sarkar S (1997) Data models in geographic information systems. Commun ACM 40 4:103–111Google Scholar
  42. 42.
    Sistla P, Wolfson O, Chamberlain S, Dao S (1997) Modeling and querying moving objects. In: ICDE, pp 422–432Google Scholar
  43. 43.
    Speicys L, Jensen CS (2008) Enabling location-based services–multi-graph representation of transportation networks. GeoInformatica 12(2):219–253CrossRefGoogle Scholar
  44. 44.
    Speicys L, Jensen CS, Kligys A (2003) Computational data modeling for network-constrained moving objects. In: ACM-GISGoogle Scholar
  45. 45.
    Stenneth L, Wolfson O, Yu P, Xu B (2011) Transportation mode detection using mobile devices and gis information. In: ACM SIGSPATIALGoogle Scholar
  46. 46.
    Su J, Xu H, Ibarra OH (2001) Moving objects: logical relationships and queries. In: SSTDGoogle Scholar
  47. 47.
    Tao Y, Papadias D, Shen Q (2002) Continuous nearest neighbor search. In: VLDBGoogle Scholar
  48. 48.
    Thiagarajan A, Madden S (2008) Querying continuous functions in a database system. In: SIGMODGoogle Scholar
  49. 49.
    Timko I, Pedersen TB (2004) Capturing complex multidimensional data in location-based data warehouses. In: GIS, pp 147–156Google Scholar
  50. 50.
    Vazirgiannis M, Wolfson O (2001) A spatiotemporal model and language for moving objects on road networks. In: SSTDGoogle Scholar
  51. 51.
    Voisard A, David B (2002) A database perspective on geospatial data modeling. TKDE 14(2):226–242Google Scholar
  52. 52.
    Wolfson O, Xu B, Chamberlain S, Jiang L (1998) Moving objects databases: issues and solutions. In: SSDBM, pp 111–122Google Scholar
  53. 53.
    Wolfson O, Chamberlain S, Kalpakis K, Yesha Y (2001) Modeling moving objects for location based services. In: IMWSGoogle Scholar
  54. 54.
    Xu J, Güting RH (2011) Infrastructures for research on multimodal moving objects. In: MDM, demo paperGoogle Scholar
  55. 55.
    Xu J, Güting RH (2012) GMOBench: a benchmark for generic moving objects. Informatik-Report 362, Fernuniversität in HagenGoogle Scholar
  56. 56.
    Xu J, Güting RH (2012) MWGen: a mini world generator. In: MDM, to appearGoogle Scholar
  57. 57.
    Yang B, Lu H, Jensen CS (2010) Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space. In: EDBTGoogle Scholar
  58. 58.
    Zhang J, Zhu M, Papadias D, Tao Y, Tee DL (2003) Location-based spatial queries. In: SIGMODGoogle Scholar
  59. 59.
    Zheng Y, Liu L, Wang L, Xie X (2008) Learning transportation mode from raw GPS data for geographic applications on the web. In: WWWGoogle Scholar
  60. 60.
    Zheng Y, Chen Y, Xie X, Ma WY (2010) Understanding transportation mode based on GPS data for web application. ACM Trans Web 4(1):1–36CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Database Systems for New Applications, Mathematics and Computer ScienceFernUniversität HagenHagenGermany

Personalised recommendations