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dc.contributor.authorKipf, Andreas
dc.contributor.authorMarcus, Ryan
dc.contributor.authorvan Renen, Alexander
dc.contributor.authorStoian, Mihail
dc.contributor.authorKemper, Alfons
dc.contributor.authorKraska, Tim
dc.contributor.authorNeumann, Thomas
dc.date.accessioned2021-09-20T18:21:43Z
dc.date.available2021-09-20T18:21:43Z
dc.identifier.urihttps://hdl.handle.net/1721.1/132294
dc.description.abstract© 2020 ACM. Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data. We introduce RadixSpline (RS), a learned index that can be built in a single pass over the data and is competitive with state-of-the-art learned index models, like RMI, in size and lookup performance. We evaluate RS using the SOSD benchmark and show that it achieves competitive results on all datasets, despite the fact that it only has two parameters.en_US
dc.language.isoen
dc.publisherACMen_US
dc.relation.isversionof10.1145/3401071.3401659en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleRadixSpline: a single-pass learned indexen_US
dc.typeArticleen_US
dc.relation.journalProceedings of the 3rd International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2020en_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-01-11T18:20:43Z
dspace.orderedauthorsKipf, A; Marcus, R; van Renen, A; Stoian, M; Kemper, A; Kraska, T; Neumann, Ten_US
dspace.date.submission2021-01-11T18:20:51Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Needed


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