Skip to main content

Supermetric Search with the Four-Point Property

  • Conference paper
  • First Online:
Similarity Search and Applications (SISAP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9939))

Included in the following conference series:

Abstract

Metric indexing research is concerned with the efficient evaluation of queries in metric spaces. In general, a large space of objects is arranged in such a way that, when a further object is presented as a query, those objects most similar to the query can be efficiently found. Most such mechanisms rely upon the triangle inequality property of the metric governing the space. The triangle inequality property is equivalent to a finite embedding property, which states that any three points of the space can be isometrically embedded in two-dimensional Euclidean space. In this paper, we examine a class of semimetric space which is finitely 4-embeddable in three-dimensional Euclidean space. In mathematics this property has been extensively studied and is generally known as the four-point property. All spaces with the four-point property are metric spaces, but they also have some stronger geometric guarantees. We coin the term supermetric space as, in terms of metric search, they are significantly more tractable. We show some stronger geometric guarantees deriving from the four-point property which can be used in indexing to great effect, and show results for two of the SISAP benchmark searches that are substantially better than any previously published.

The term supermetric space has previously been used in the domains of particle physics and evolutionary biology as a pseudonym for the mathematical term ultra-metric, a concept of no interest in metric search; we believe our concept is of sufficient importance to the domain to justify its reuse with a different meaning.

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

Access this chapter

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.

    All of the (Java) code for these experiments can be downloaded from https://bitbucket.org/richardconnor/metric-space-framework/.

  2. 2.

    Which we therefore believe makes the best performance yet published for these metric/dataset combinations.

References

  1. Brin, S.: Near neighbor search in large metric spaces. In 21th International Conference on Very Large Data Bases (VLDB 1995) (1995)

    Google Scholar 

  2. Chávez, E., Ludueña, V., Reyes, N., Roggero, P.: Faster proximity searching with the distal SAT. Inf. Syst. 59, 15–47 (2016)

    Article  Google Scholar 

  3. Chávez, E., Navarro, G.: Metric databases. In: Rivero, L.C., Doorn, J.H., Ferraggine, V.E. (eds.) Encyclopedia of Database Technologies and Applications, pp. 366–371. Idea Group, Hershey (2005)

    Google Scholar 

  4. Connor, R., Cardillo, F.A., Vadicamo, L., Rabitti, F.: Hilbert exclusion: improved metric search through finite isometric embeddings. ArXiv e-prints (accepted for publication ACM TOIS, July 2016), April 2016

    Google Scholar 

  5. Figueroa, K., Navarro, G., Chávez, E.: Metric spaces library. www.sisap.org/library/manual.pdf

  6. Noltemeier, H., Verbarg, K., Zirkelbach, C.: Monotonous Bisector* Trees — a tool for efficient partitioning of complex scenes of geometric objects. In: Monien, B., Ottmann, Th (eds.) Data Structures and Efficient Algorithms. LNCS, vol. 594, pp. 186–203. Springer, Heidelberg (1992). doi:10.1007/3-540-55488-2_27

    Chapter  Google Scholar 

  7. Novak, D., Batko, M., Zezula, P.: Metric index: an efficient and scalable solution for precise and approximate similarity search. Inf. Syst. 36(4), 721–733 (2011). Selected Papers from the 2nd International Workshop on Similarity Search and Applications SISAP (2009)

    Article  Google Scholar 

  8. Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32. Springer, New York (2006)

    MATH  Google Scholar 

Download references

Acknowledgements

We would like to thank the anonymous referees for helpful comments on an earlier version of this paper. Richard Connor would like to acknowledge support by the National Research Council of Italy (CNR) for a Short-term Mobility Fellowship (STM) in June 2015, which funded a stay at ISTI-CNR in Pisa during which much of this work was conceived.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Richard Connor .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Connor, R., Vadicamo, L., Cardillo, F.A., Rabitti, F. (2016). Supermetric Search with the Four-Point Property. In: Amsaleg, L., Houle, M., Schubert, E. (eds) Similarity Search and Applications. SISAP 2016. Lecture Notes in Computer Science(), vol 9939. Springer, Cham. https://doi.org/10.1007/978-3-319-46759-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46759-7_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46758-0

  • Online ISBN: 978-3-319-46759-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics