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GeoInformatica

, Volume 9, Issue 1, pp 33–60 | Cite as

Indexing the Trajectories of Moving Objects in Networks*

  • Victor Teixeira de AlmeidaEmail author
  • Ralf Hartmut Güting
Original Article

Abstract

The management of moving objects has been intensively studied in recent years. A wide and increasing range of database applications has to deal with spatial objects whose position changes continuously over time, called moving objects. The main interest of these applications is to efficiently store and query the positions of these continuously moving objects. To achieve this goal, index structures are required. The main proposals of index structures for moving objects deal with unconstrained 2-dimensional movement. Constrained movement is a special and a very important case of object movement. For example, cars move in roads and trains in railroads. In this paper we propose a new index structure for moving objects on networks, the MON-Tree. We describe two network models that can be indexed by the MON-Tree. The first model is edge oriented, i.e., the network consists of nodes and edges and there is a polyline associated to each edge. The second one is more suitable for transportation networks and is route oriented, i.e., the network consists of routes and junctions. In this model, a polyline also serves as a representation of the routes. We propose the index in terms of the basic algorithms for insertion and querying. We test our proposal in an extensive experimental evaluation with generated data sets using as underlying networks the roads of Germany. In our tests, the MON-Tree shows good scalabiliy and outperforms the competing index structures in updating (index creation) as well as in querying.

Keywords

spatio-temporal databases moving objects in networks index structures 

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References

  1. 1.
    N. Beckmann, H.P. Kriegel, R. Schneider, and B. Seeger. “The R*-tree: An efficient and robust access method for points and rectangles,” in Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 322–331, 1990.Google Scholar
  2. 2.
    T. Brinkhoff. “A framework for generating network-based moving objects,” Geolnfomatica, Vol. 6(2):153–180, 2002.Google Scholar
  3. 3.
    V.P. Chakka, A. Everspaugh, and J.M. Patel. “Indexing large trajectory data sets with SETI,” in Proc. of the First Biennial Conference on Innovative Data Systems Research (CIDR), 2003.Google Scholar
  4. 4.
    H.D. Chon, D. Agrawal, and A.E. Abbadi. “Using space-time grid for efficient management of moving objects,” in 2nd ACM Intl. Workshop on Data Engineering for Wireless and Mobile Access (MobiDE), pp. 59–65, 2001.Google Scholar
  5. 5.
    H.D. Chon, D. Agrawal, and A.E. Abbadi. “Query processing for moving objects with space-time grid storage model,” in Proc. of the 3rd Intl. Conf. on Mobile Data Management (MDM), pp. 121–128, 2002.Google Scholar
  6. 6.
    J. A. Cotelo Lema, L. Forlizzi, R. H. Güting, E. Nardelli, and M. Schneider. “Algorithms for moving objects databases,” The Computer Journal, Vol. 46(6):68–712, 2003.Google Scholar
  7. 7.
    V.T. de Almeida and R.H. Güting. “Indexing the trajectories of moving objects in networks (Extended Abstract),” in Proc. of the 16th Intl. Conf. on Scientific and Statistical Database Management (SSDBM), pp. 115–118, 2004.Google Scholar
  8. 8.
    M. Erwig, R. H. Güting, M. Schneider, and M. Vazirgiannis. “Spatio-temporaI data types: An approach to modeling and querying moving objects in databases,” Geolnformatica, Vol. 3(3):269-296, 1999. Google Scholar
  9. 9.
    L. Forlizzi, R.H. Güting, E. Nardelli, and M. Schneider. “A data model and data structures for moving objects databases,” in Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, Vol. 29(2), pp. 319–330, 2000.Google Scholar
  10. 10.
    E. Frentzos. “Indexing objects moving on fixed networks,” in Proc. of the 8th Intl. Symp. on Spatial and Temporal Databases (SSTD), pp. 289–305, 2003.Google Scholar
  11. 11.
    R. H. Güting, M. H. Böhlen, M. Erwig, C. S. Jensen, N. A. Lorentzos, M. Schneider, and M. Vazirgiannis. “A foundation for representing and querying moving objects,” ACM Transactions on Database Systems (TODS), Vol. 25(1):1–42, 2000. Google Scholar
  12. 12.
    R.H. Güting, V.T. de Almeida, and Z. Ding. Modeling and querying moving objects in networks. Technical Report 308, Fernuniversität Hagen, Fachbereich Informatik, 2004.Google Scholar
  13. 13.
    M. Hadjieleftheriou, G. Kollios, V.J. Tsotras, and G. Gunopulos. “Efficient indexing of spatiotemporal objects,” in Proc. of the 8th Intl. Conf. on Extending Database Technology (EDBT), pp. 251–268, 2002.Google Scholar
  14. 14.
    C. Hage, C.S. Jensen, T.B. Pedersen, L. Speicys, and I. Timko. “Integrated data management for mobile services in the real world,” in Proc. of 21th Intl. Conf on Very Large Data Bases (VLDB), pp. 1019–1030, 2003.Google Scholar
  15. 15.
    C.S. Jensen and D. Pfoser. “Indexing of network constrained moving objects,” in Proc. of the 11th Intl. Symp. on Advances in Geographic Information Systems (ACM-GIS), 2003.Google Scholar
  16. 16.
    I. Kamel and C. Faloutsos. “On packing R-trees,” in Proc. of the 2nd Intl. Conf. on Information and Knowledge Management (CIKM), pp. 490–499, 1993.Google Scholar
  17. 17.
    G. Kollios, D. Gunopulos, and V.J. Tsotras. “On indexing mobile objects,” in Proc. of ACM Symp. on Principles of Database Systems (PODS), pp. 261–272, 1999.Google Scholar
  18. 18.
    S. T. Leutenegger and M.A. Lopez. “The effect of buffering on the performance of r-trees,” Knowledge and Data Engineering, Vol. 12(1):33–44, 2000. Google Scholar
  19. 19.
    D. Papadias, J. Zhang, N. Mamoulis, and Y. Tao. “Query processing in spatial network databases,” in Proc. of 29th Intl. Conf. on Very Large Data Bases (VLDB), pp. 802–813, 2003.Google Scholar
  20. 20.
    A. Papadopoulos and Y. Manolopoulos. “Multiple range query optimization in spatial databases,” in Proc. of the 2nd East European Symp. on Advances in Databases and Information Systems (ADBIS), pp. 71–82, 1998.Google Scholar
  21. 21.
    D. Papadopoulos, G. Kollios, D. Gunopulos, and V.J. Tsotras. “Indexing mobile objects on the plane,” in Proc. of the 13th Intl. Workshop on Database and Expert Systems Applications (DEXA), pp. 693–697, 2002.Google Scholar
  22. 22.
    D. Pfoser and C.S. Jensen. “Capturing the uncertainty of moving-object representations,” in Proc. of Advances in Spatial Databases, 6th Intl. Symp. (SSD), pp. 111–132, 1999.Google Scholar
  23. 23.
    D. Pfoser and C.S. Jensen. “Querying the trajectories of on-line mobile objects,” in Proc. of the 2nd ACM Intl. Workshop on Data Engineering for Wireless and Mobile Access (MobiDE), pp. 66–73, 2001.Google Scholar
  24. 24.
    D. Pfoser, C.S. Jensen, and Y. Theodoridis. “Novel approaches in query processing for moving object trajectories,” in Proc. of 26th Intl. Conf. on Very Large Data Bases (VLDB), pp. 395–406, 2000.Google Scholar
  25. 25.
    C.M. Procopiuc, P.K. Agarwal, and S. Har-Peled. “STAR-tree: An efficient self-adjusting index for moving objects,” in Algorithm Engineering and Experiments, 4th Intl. Workshop (ALENEX), pp. 178–193, 2002.Google Scholar
  26. 26.
    S. Saltenis and C.S. Jensen. “Indexing of moving objects for location-based services,” in Proc. of the 18th Intl. Conf. on Data Engineering, pp. 463–472, 2002.Google Scholar
  27. 27.
    S. Saltenis, C.S. Jensen, S.T. Leutenegger, and M.A. Lopez. “Indexing the positions of continuously moving objects,” in Proc. of the SIGMOD Intl. Conf. on Management of Data, pp. 331–342, 2000.Google Scholar
  28. 28.
    A.P. Sistla, O. Wolfson, S. Chamberlain, and S. Dao. “Querying the uncertain position of moving objects,” in Temporal Databases: Research and Practice, Vol. 1399, pp. 310–337. LNCS, 1998.Google Scholar
  29. 29.
    Z. Song and N. Roussopoulos. “Hashing moving objects,” in Proc. of the 2nd Intl. Conf. on Mobile Data Management (MDM), pp. 161–172, 2001.Google Scholar
  30. 30.
    Z. Song and N. Roussopoulos. “SEB-tree: An approach to index continuously moving objects,” in Proc. of the 4th Intl. Conf. on Mobile Data Management (MDM), pp. 340–344, 2003.Google Scholar
  31. 31.
    Y. Tao, D. Papadias, and J. Sun. “The TPR*-tree: An optimized spatio-temporal access method for predictive queries,” in Proc. of the 29th Very Large Data Bases Conference (VLDB), pp. 790–801, 2003.Google Scholar
  32. 32.
    J. Tayeb, Ö. Ulusoy, and O. Wolfson. “A quadtree-based dynamic attribute indexing method,” The Computer Journal, Vol. 41(3):185–200, 1998. Google Scholar
  33. 33.
    Y. Theodoridis, T.K. Sellis, A. Papadopoulos, and Y. Manolopoulos. “Specifications for efficient indexing in spatiotemporal databases,” in Proc. of the 10th Intl. Conf. on Scientific and Statistical Database Management (SSDBM), pp. 123–132, 1998.Google Scholar
  34. 34.
    G. Trajcevski, O. Wolfson, F. Zhang, and S. Chamberlain. “The geometry of uncertainty in moving objects databases,” in Proc. of the 8th Intl. Conf. on Extending Database Technology (EDBT), pp. 233–250, 2002.Google Scholar
  35. 35.
    O. Wolfson, S. Chamberlain, S. Dao, L. Jiang, and G. Mendez. “Cost and imprecision in modeling the position of moving objects,” in Proc. of the 14th Intl. Conf. on Data Engineering, pp. 588–596, 1998.Google Scholar
  36. 36.
    0. Wolfson, A. P. Sistla, S. Chamberlain, and Y. Yeshs. “Updating and querying databases that track mobile units,” Distributed and Parallel Databases, Vol. 7(3):257–387, 1999.Google Scholar
  37. 37.
    O. Wolfson, B. Xu, S. Chamberlain, and L. Jiang. “Moving objects databases: Issues and solutions,” in Proc. of the 10th Intl. Conf. on Scientific and Statistical Database Management (SSDBM), pp. 111–122, 1998.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Victor Teixeira de Almeida
    • 1
    Email author
  • Ralf Hartmut Güting
    • 1
  1. 1.Praktische Informatik IVFernuniversität HagenHagenGermany

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