Querying metric spaces with bit operations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
3 Downloads (Pure)


Metric search techniques can be usefully characterised by the time at which distance calculations are performed during a query. Most exact search mechanisms use a “just-in-time” approach where distances are calculated as part of a navigational strategy. An alternative is to use a “one-time” approach, where distances to a fixed set of reference objects are calculated at the start of each query. These distances are typically used to re-cast data and queries into a different space where querying is more efficient, allowing an approximate solution to be obtained.

In this paper we use a “one-time” approach for an exact search mechanism. A fixed set of reference objects is used to define a large set of regions within the original space, and each query is assessed with respect to the definition of these regions. Data is then accessed if, and only if, it is useful for the calculation of the query solution.

As dimensionality increases, the number of defined regions must increase, but the memory required for the exclusion calculation does not. We show that the technique gives excellent performance over the SISAP benchmark data sets, and most interestingly we show how increases in dimensionality may be countered by relatively modest increases in the number of reference objects used.
Original languageEnglish
Title of host publicationSimilarity Search and Applications
Subtitle of host publication11th International Conference, SISAP 2018, Lima, Peru, October 7-9, 2018, Proceedings
EditorsStéphane Marchand-Maillet, Yasin N. Silva, Edgar Chávez
Place of PublicationCham
Number of pages14
ISBN (Electronic)9783030022242
ISBN (Print)9783030022235
Publication statusPublished - 2018
Event11th International Conference on Similarity Search and Applications (SISAP 2018) - Lima, Peru
Duration: 7 Oct 20189 Oct 2018
Conference number: 11

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Similarity Search and Applications (SISAP 2018)
Abbreviated titleSISAP 2018
Internet address


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