Streaming Quantiles Algorithms with Small Space and Update Time

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dc.citation.articleNumber9612
dc.citation.issueNumber24
dc.citation.journalTitleSensors
dc.citation.volumeNumber22
dc.contributor.authorIvkin, Nikita
dc.contributor.authorLiberty, Edo
dc.contributor.authorLang, Kevin
dc.contributor.authorKarnin, Zohar
dc.contributor.authorBraverman, Vladimir
dc.date.accessioned2023-01-27T14:47:42Z
dc.date.available2023-01-27T14:47:42Z
dc.date.issued2022
dc.description.abstractApproximating quantiles and distributions over streaming data has been studied for roughly two decades now. Recently, Karnin, Lang, and Liberty proposed the first asymptotically optimal algorithm for doing so. This manuscript complements their theoretical result by providing a practical variants of their algorithm with improved constants. For a given sketch size, our techniques provably reduce the upper bound on the sketch error by a factor of two. These improvements are verified experimentally. Our modified quantile sketch improves the latency as well by reducing the worst-case update time from O(1ε) down to O(log1ε).
dc.identifier.citationIvkin, Nikita, Liberty, Edo, Lang, Kevin, et al.. "Streaming Quantiles Algorithms with Small Space and Update Time." <i>Sensors,</i> 22, no. 24 (2022) MDPI: https://doi.org/10.3390/s22249612.
dc.identifier.digitalsensors-22-09612-v2
dc.identifier.doihttps://doi.org/10.3390/s22249612
dc.identifier.urihttps://hdl.handle.net/1911/114303
dc.language.isoeng
dc.publisherMDPI
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleStreaming Quantiles Algorithms with Small Space and Update Time
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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