Multiversion Linear Quadtree for Spatio-Temporal Data

  • Theodoros Tzouramanis
  • Michael Vassilakopoulos
  • Yannis Manolopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1884)


Research in spatio-temporal databases has largely focused on extensions of access methods for the proper handling of time changing spatial information. In this paper, we present the Multiversion Linear Quadtree (MVLQ), a spatio-temporal access method based on Multiversion B-trees (MVBT) [2], embedding ideas from Linear Region Quadtrees [4]. More specifically, instead of storing independent numerical data having a different transaction-time each, for every consecutive image we store a group of codewords that share the same transaction-time, whereas each codeword represents a spatial subregion. Thus, the new structure may be used as an index mechanism for storing and accessing evolving raster images. We also conducted a thorough experimentation using sequences of real and synthetic raster images. In particular, we examined the time performance of temporal window queries, and provide results for a variety of parameter settings.


Geographical Information System Query Processing Raster Image Node Capacity Naive Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    T. Abraham and J.F. Roddick: Survey of Spatio-Temporal Databases, Geoinformatica, Vol. 3, No. 1, pp. 61–99, 1999CrossRefGoogle Scholar
  2. 2.
    B. Becker, S. Gschwind, T. Ohler, B. Seeger and P. Widmayer: An Asymptotically Optimal Multiversion B-tree, The VLDB Journal, Vol. 5, No. 4, pp. 264–275, 1996.CrossRefGoogle Scholar
  3. 3.
    J.R. Driscoll, N. Sarnak, D.D. Sleator, and R.E. Tarjan: Making Data Structures Persistent, Journal of Computer and System Sciences, Vol. 38, pp. 86–124, 1989.zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    I. Gargantini: An Effective Way to Represent Quadtrees, Communications of the ACM, Vol. 25, No. 12, pp. 905–910, 1982.zbMATHCrossRefGoogle Scholar
  5. 5.
    V. Gaede and O. Guenther: Multidimensional Access Methods, ACM Computer Surveys, Vol. 30, No. 2, pp. 123–169, 1998.CrossRefGoogle Scholar
  6. 6.
    C.S. Jensen et al.: A Consensus Glossary of Temporal Database Concepts, ACM SIGMOD Record, Vol. 23, No. 1, pp. 52–64, 1994.CrossRefGoogle Scholar
  7. 7.
    E. Kawaguchi and T. Endo: On a Method of Binary Picture Representation and its Application to Data Compression, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 2, No. 1, pp. 27–35, 1980.zbMATHCrossRefGoogle Scholar
  8. 8.
    S.D. Lang and J.R. Driscoll: Improving the Differential File Technique via Batch Operations for Tree Structured File Organizations, Proceedings IEEE International Conference on Data Engineering (ICDE’86), Los Angeles, CA, 1986.Google Scholar
  9. 9.
    Y. Manolopoulos, E. Nardelli, G. Proietti and M. Vassilakopoulos: On the Generation of Aggregated Random Spatial Regions, Proceedings 4th International Conference on Information and Knowledge Management (CIKM’95), pp. 318–325, Washington DC, 1995.Google Scholar
  10. 10.
    H. Samet: The Design and Analysis of Spatial Data Structures, Addison-Wesley, Reading MA, 1990.Google Scholar
  11. 11.
    H. Samet: Applications of Spatial Data Structures, Addison-Wesley, Reading MA, 1990.Google Scholar
  12. 12.
    B. Saltzberg and V. Tsotras: A Comparison of Access Methods for Time Evolving Data, ACM Computing Surveys, Vol. 31, No. 2, pp. 158–221, 1999.CrossRefGoogle Scholar
  13. 13.
    T. Tzouramanis, Y. Manolopoulos and N. Lorentzos: Overlapping B+-trees: an Implementation of a Temporal Access Method’, Data and Knowledge Engineering, Vol. 29, No. 3, pp. 381–404, 1999.zbMATHCrossRefGoogle Scholar
  14. 14.
    T. Tzouramanis, M. Vassilakopoulos and Y. Manolopoulos: Overlapping Linear Quadtrees: a Spatio-temporal Access Method, Proceedings 6th ACM Symposium on Advances in Geographic Information Systems (ACM-GIS’98), pp. 1–7, Bethesda MD, November 1998.Google Scholar
  15. 15.
    T. Tzouramanis, M. Vassilakopoulos and Y. Manolopoulos: Processing of Spatio-Temporal Queries in Image Databases, Proceedings 3rd East-European Conference on Advances in Databases and Information Systems (ADBIS’99), pp. 85–97, Maribor, September 1999.Google Scholar
  16. 16.
    T. Tzouramanis, M. Vassilakopoulos and Y. Manolopoulos: Multiversion Linear Quadtree for Spatio-Temporal Data, Technical Report, Data Engineering Lab, Department of Informatics, Aristotle University of Thessaloniki. Address for downloading:
  17. 17.
    T. Tzouramanis, M. Vassilakopoulos and Y. Manolopoulos: Overlapping Linear Quadtrees and Window Query Processing in Spatio-Temporal Databases, submitted.Google Scholar
  18. 18.
    Y. Theodoridis, T. Sellis, A. Papadopoulos and Y. Manolopoulos: Specifications for Efficient Indexing in Spatiotemporal Databases, Proceedings of the 7th Conference on Statistical and Scientific Database Management Systems (SSDBM’98), pp. 123–132, Capri, Italy, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Theodoros Tzouramanis
    • 1
  • Michael Vassilakopoulos
    • 1
  • Yannis Manolopoulos
    • 1
  1. 1.Data Engineering Lab Department of InformaticsAristotle UniversityThessalonikiGreece

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