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dc.contributor.authorVogelsgesang, Adrian
dc.contributor.authorMuehlbauer, Tobias
dc.contributor.authorLeis, Viktor
dc.contributor.authorNeumann, Thomas
dc.contributor.authorKemper, Alfons
dc.contributor.editorGrust, Torsten
dc.contributor.editorNaumann, Felix
dc.contributor.editorBöhm, Alexander
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorHärder, Theo
dc.contributor.editorRahm, Erhard
dc.contributor.editorHeuer, Andreas
dc.contributor.editorKlettke, Meike
dc.contributor.editorMeyer, Holger
dc.date.accessioned2019-04-11T07:21:21Z
dc.date.available2019-04-11T07:21:21Z
dc.date.issued2019
dc.identifier.isbn978-3-88579-683-1
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/21703
dc.description.abstractThe Hyper database system was started as an academic project at Technical University Munich. In 2016, the commercial spin-off of the academic Hyper database system was acquired by Tableau, a leader in the analytics and business intelligence (BI) platforms market. As a human-in-the-loop BI platform, Tableau products machine-generate query workloads with characteristics that differ from human-written queries and queries represented in industry-standard database system benchmarks. In this work, we contribute optimizations we developed for one important class of queries typically generated by Tableau products: retrieving (aggregates of) the domain of a column. We devise methods for leveraging the compression of the database column in order to efficiently retrieve the duplicate-free value set, i.e., the domain. Our extensive performance evaluation of a synthetic benchmark and over 60 thousand real-world workbooks from Tableau Public shows that our optimization enables query latencies for domain queries that allow self-service ad-hoc data exploration.en
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2019
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) – Proceedings, Volume P-289
dc.titleDomain Query Optimization: Adapting the General-Purpose Database System Hyper for Tableau Workloadsen
mci.reference.pages313-333
mci.conference.sessiontitleIndustriebeiträge
mci.conference.locationRostock
mci.conference.date4.-8. März 2019
dc.identifier.doi10.18420/btw2019-19


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