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dc.contributor.authorConnor, Richard
dc.contributor.authorDearle, Al
dc.contributor.authorVadicamo, Lucia
dc.contributor.editorAmato, Giuseppe
dc.contributor.editorBartalesi, Valentina
dc.contributor.editorBianchini, Devis
dc.contributor.editorGennaro, Claudio
dc.contributor.editorTorlone, Riccardo
dc.identifier.citationConnor , R , Dearle , A & Vadicamo , L 2022 , Investigating binary partition power in metric query . in G Amato , V Bartalesi , D Bianchini , C Gennaro & R Torlone (eds) , SEBD 2022 : proceedings of the the 30 th Italian Symposium on Advanced Database Systems . vol. 3194 , Italian Symposium on Advanced Database Systems , vol. 3194 , CEUR-WS , Online , pp. 415-426 , Italian Symposium on Advanced Database Systems (SEBD 2022) , Tirrenia (Pisa) , Italy , 19/06/22 . < >en
dc.identifier.otherPURE: 280285624
dc.identifier.otherPURE UUID: 9b31ab59-9e9d-4e24-9995-d8eb57b0038a
dc.identifier.otherScopus: 85137429305
dc.description.abstractIt is generally understood that, as dimensionality increases, the minimum cost of metric query tends from (log ) to () in both space and time, where is the size of the data set. With low dimensionality, the former is easy to achieve; with very high dimensionality, the latter is inevitable. We previously described BitPart as a novel mechanism suitable for performing exact metric search in “high(er)” dimensions. The essential tradeoff of BitPart is that its space cost is linear with respect to the size of the data, but the actual space required for each object may be small as log2 bits, which allows even very large data sets to be queried using only main memory. Potentially the time cost still scales with (log ). Together these attributes give exact search which outperforms indexing structures if dimensionality is within a certain range. In this article, we reiterate the design of BitPart in this context. The novel contribution is an in-depth examination of what the notion of “high(er)” means in practical terms. To do this we introduce the notion of exclusion power, and show its application to some generated data sets across different dimensions.
dc.relation.ispartofSEBD 2022en
dc.relation.ispartofseriesItalian Symposium on Advanced Database Systemsen
dc.rightsCopyright © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).en
dc.subjectSimilarity searchen
dc.subjectExclusion poweren
dc.subjectMetric searchen
dc.subjectMetric indexingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectZA4450 Databasesen
dc.titleInvestigating binary partition power in metric queryen
dc.typeConference itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen

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