Skip to main content
SpringerLink
Log in

Dynamic Distal Spatial Approximation Trees

  • Conference paper
  • First Online:
Computer Science – CACIC 2022 (CACIC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1778))

Included in the following conference series:

  • 101 Accesses

Abstract

Metric space indexes are critical for efficient similarity searches across various applications. The Distal Spatial Approximation Tree (DiSAT) has demonstrated exceptional speed/memory trade-offs without requiring parameter tuning. However, since it operates solely on static databases, its application is limited in many exciting use cases.

This research has been dedicated to developing a dynamic version of DiSAT that allows for incremental construction. It is remarkable that the dynamic version is faster than its static counterpart. The outcome is a faster index with the same memory requirements as DiSAT. This development enhances the practicality of DiSAT, unlocking a wide range of proximity database applications.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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.

    At http://www-db.deis.unibo.it/research/Mtree/.

References

  1. Bentley, J.L., Saxe, J.B.: Decomposable searching problems I. Static-to-dynamic transformation. J. Algorithms 1(4), 301–358 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  2. Chávez, E., Di Genaro, M.E., Reyes, N.: An efficient dynamic version of the distal spatial approximation trees. In: Actas del XXVIII Congreso Argentino de Ciencias de la Computación (CACIC 2022), pp. 468–477, October 2022

    Google Scholar 

  3. Chávez, E., Di Genaro, M.E., Reyes, N., Roggero, P.: Decomposability of disat for index dynamization. J. Comput. Sci. Technol. 110–116 (2017)

    Google Scholar 

  4. Chávez, E., Ludeña, V., Reyes, N., Roggero, P.: Faster proximity searching with the distal sat. Inf. Syst. (2016)

    Google Scholar 

  5. Chávez, E., Navarro, G.: A compact space decomposition for effective metric indexing. Pattern Recogn. Lett. 26(9), 1363–1376 (2005)

    Article  Google Scholar 

  6. Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.L.: Searching in metric spaces. ACM Comput. Surv. 33(3), 273–321 (2001)

    Article  Google Scholar 

  7. Chen, L., et al.: Indexing metric spaces for exact similarity search. ACM Comput. Surv. 55(6), 1–39 (2022)

    Article  MathSciNet  Google Scholar 

  8. Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: Proceedings of the 23rd Conference on Very Large Databases (VLDB 1997), pp. 426–435 (1997)

    Google Scholar 

  9. Navarro, G.: Searching in metric spaces by spatial approximation. Very Large Databases J. (VLDBJ) 11(1), 28–46 (2002)

    Article  Google Scholar 

  10. Navarro, G., Reyes, N.: Dynamic spatial approximation trees. J. Exp. Algorithmics 12, 1.5:1–1.5:68 (2008)

    Google Scholar 

  11. Samet, H.: Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling). Morgan Kaufmann Publishers Inc., San Francisco (2005)

    Google Scholar 

  12. Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Inf. Process. Lett. 40, 175–179 (1991)

    Article  MATH  Google Scholar 

  13. Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of the 4th ACM-SIAM Symposium on Discrete Algorithms (SODA 1993), pp. 311–321 (1993)

    Google Scholar 

  14. Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32. Springer, New York (2006). https://doi.org/10.1007/0-387-29151-2

Download references

Author information

Authors and Affiliations

  1. Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Mexico

    Edgar Chávez

  2. Departamento de Informática, Universidad Nacional de San Luis, San Luis, Argentina

    María E. Di Genaro & Nora Reyes

Authors
  1. Edgar Chávez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. María E. Di Genaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nora Reyes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nora Reyes .

Editor information

Editors and Affiliations

  1. Universidad Nacional de La Plata, La Plata, Argentina

    Patricia Pesado

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chávez, E., Di Genaro, M.E., Reyes, N. (2023). Dynamic Distal Spatial Approximation Trees. In: Pesado, P. (eds) Computer Science – CACIC 2022. CACIC 2022. Communications in Computer and Information Science, vol 1778. Springer, Cham. https://doi.org/10.1007/978-3-031-34147-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34147-2_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34146-5

  • Online ISBN: 978-3-031-34147-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation