Streaming Quantiles Algorithms with Small Space and Update Time

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dc.citation.articleNumber9612en_US
dc.citation.issueNumber24en_US
dc.citation.journalTitleSensorsen_US
dc.citation.volumeNumber22en_US
dc.contributor.authorIvkin, Nikitaen_US
dc.contributor.authorLiberty, Edoen_US
dc.contributor.authorLang, Kevinen_US
dc.contributor.authorKarnin, Zoharen_US
dc.contributor.authorBraverman, Vladimiren_US
dc.date.accessioned2023-01-27T14:47:42Zen_US
dc.date.available2023-01-27T14:47:42Zen_US
dc.date.issued2022en_US
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).en_US
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.en_US
dc.identifier.digitalsensors-22-09612-v2en_US
dc.identifier.doihttps://doi.org/10.3390/s22249612en_US
dc.identifier.urihttps://hdl.handle.net/1911/114303en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleStreaming Quantiles Algorithms with Small Space and Update Timeen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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