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Aggregate-based Training Phase for ML-based Cardinality Estimation

dc.contributor.authorWoltmann, Lucas
dc.contributor.authorHartmann, Claudio
dc.contributor.authorHabich, Dirk
dc.contributor.authorLehner, Wolfgang
dc.contributor.editorKai-Uwe Sattler
dc.contributor.editorMelanie Herschel
dc.contributor.editorWolfgang Lehner
dc.date.accessioned2021-03-16T07:57:13Z
dc.date.available2021-03-16T07:57:13Z
dc.date.issued2021
dc.description.abstractCardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lot of training queries have to be executed during the model training phase to learn a data-dependent ML model making it very time-consuming. Many of those training or example queries use the same base data, have the same query structure, and only differ in their selective predicates. To speed up the model training phase, our core idea is to determine a predicate-independent pre-aggregation of the base data and to execute the example queries over this pre-aggregated data. Based on this idea, we present a specific aggregate-based training phase for ML-based cardinality estimation approaches in this paper. As we are going to show with different workloads in our evaluation, we are able to achieve an average speedup of 63 with our aggregate-based training phase and thus outperform indexes.en
dc.identifier.doi10.18420/btw2021-07
dc.identifier.isbn978-3-88579-705-0
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/35812
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2021
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-311
dc.subjectcardinality estimation
dc.subjectmachine learning
dc.subjectdatabase support
dc.subjectpre-aggregation
dc.titleAggregate-based Training Phase for ML-based Cardinality Estimationen
gi.citation.endPage154
gi.citation.startPage135
gi.conference.date13.-17. September 2021
gi.conference.locationDresden
gi.conference.sessiontitleML & Data Science

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