Skip to main content
SpringerLink
Log in

The xBR\(^+\)-tree: An Efficient Access Method for Points

  • Conference paper
  • First Online:
Database and Expert Systems Applications (Globe 2015, DEXA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9261))

Abstract

Spatial indexes, such as those based on Quadtree, are important in spatial databases for efficient execution of queries involving spatial constraints. In this paper, we present improvements of the xBR-tree (a member of the Quadtree family) with modified internal node structure and tree building process, called xBR\(^+\)-tree. We highlight the differences of the algorithms for processing single dataset queries between the xBR and xBR\(^+\)-trees and we demonstrate performance results (I/O efficiency and execution time) of extensive experimentation (based on real and synthetic datasets) on tree building process and processing of single dataset queries, using the two structures. These results show that the two trees are comparable, regarding their building performance, however, the xBR\(^+\)-tree is an overall winner, regarding spatial query processing.

G. Roumelis, M. Vassilakopoulos, T. Loukopoulos, A. Corral and Y. Manolopoulos—Work funded by the GENCENG project (SYNERGASIA 2011 action, supported by the European Regional Development Fund and Greek National Funds); project number 11SYN_ 8_1213.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Note that we used double numbers for coordinates, instead of float numbers used in [10], to be able to represent large number of points in the normalized square space. Due to this change of representation, results for specific datasets that appear in [10] for the xBR-tree differ slightly from the respective results in this paper.

References

  1. Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. In: SIGMOD Conference, pp. 322–331 (1990)

    Google Scholar 

  2. Finkel, R., Bentley, J.: Quad trees: a data structure for retrieval on composite keys. Acta Informatica 4, 1–9 (1974)

    Article  Google Scholar 

  3. Gaede, V., Gunther, O.: Multidimensional access methods. ACM Comput. Surv. 30(2), 170–231 (1998)

    Article  Google Scholar 

  4. Gorawski, M., Bugdol, M.: Cost Model for XBR-tree. In: Kozielski, S., Wrembel, R. (eds.) New Trends in Data Warehousing and Data Analysis. Springer, USA (2009)

    Google Scholar 

  5. Hoel, E.G., Samet, H.: A qualitative comparison study of data structures for large line segment databases. In: SIGMOD Conference, pp. 205–214 (1992)

    Google Scholar 

  6. Hoel, E.G., Samet, H.: Benchmarking spatial join operations with spatial output. In: VLDB Conference, pp. 606–618 (1995)

    Google Scholar 

  7. Kim, Y.J., Patel, J.: Performance comparison of the R*-tree and the quadtree for kNN and distance join queries. IEEE Trans. Knowl. Data Eng. 22(7), 1014–1027 (2010)

    Article  Google Scholar 

  8. Manolopoulos, Y., Nanopoulos, A., Papadopoulos, A., Theodoridis, Y.: R-Trees: Theory and Applications. Springer, London (2006)

    Book  Google Scholar 

  9. Roumelis, G., Vassilakopoulos, M., Corral, A.: Algorithms for processing Nearest Neighbor Queries using xBR-trees. In: Panhellenic Conference on Informatics, pp. 51–55 (2011)

    Google Scholar 

  10. Roumelis, G., Vassilakopoulos, M., Corral, A.: Performance comparison of xBR-trees and R*-trees for single dataset spatial queries. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds.) ADBIS 2011. LNCS, vol. 6909, pp. 228–242. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Samet, H.: The Design and Analysis of Spatial Data Structures. Addison-Wesley, Boston (1990)

    Google Scholar 

  12. Samet, H.: Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS. Addison-Wesley, Boston (1990)

    Google Scholar 

  13. Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann Publishers, San Francisco (2006)

    MATH  Google Scholar 

  14. Samet, H.: The quadtree and related hierarchical data structure. Comput. Surv. 16(2), 187–260 (1984)

    Article  MathSciNet  Google Scholar 

  15. Vassilakopoulos, M., Manolopoulos, Y.: External balanced regular (x-BR) trees: new structures for very large spatial databases. In: Panhellenic Conference on Informatics, pp. 324–333 (2000)

    Google Scholar 

  16. Yin, X., Düntsch, I., Gediga, G.: Quadtree representation and compression of spatial data. In: Peters, J.F., Skowron, A., Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) Transactions on Rough Sets XIII. LNCS, vol. 6499, pp. 207–239. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    George Roumelis & Yannis Manolopoulos

  2. Department of Electrical and Computer Engineering, University of Thessaly, Volos, Greece

    Michael Vassilakopoulos

  3. Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

    Thanasis Loukopoulos

  4. Department of Informatics, University of Almeria, Almeria, Spain

    Antonio Corral

Authors
  1. George Roumelis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Vassilakopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thanasis Loukopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonio Corral
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yannis Manolopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Vassilakopoulos .

Editor information

Editors and Affiliations

  1. Hewlett-Packard Enterprise, Sunnyvale, California, USA

    Qiming Chen

  2. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  3. Blaise Pascal University, Aubiere, France

    Farouk Toumani

  4. University of Linz, Linz, Austria

    Roland Wagner

  5. Universidad Politécnica de Valencia, Valencia, Spain

    Hendrik Decker

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Roumelis, G., Vassilakopoulos, M., Loukopoulos, T., Corral, A., Manolopoulos, Y. (2015). The xBR\(^+\)-tree: An Efficient Access Method for Points. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22849-5_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22848-8

  • Online ISBN: 978-3-319-22849-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation