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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30586
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dc.contributor.authorLehmann, Claude-
dc.contributor.authorSulimov, Pavel-
dc.contributor.authorStockinger, Kurt-
dc.date.accessioned2024-05-02T12:53:01Z-
dc.date.available2024-05-02T12:53:01Z-
dc.date.issued2024-03-
dc.identifier.issn2150-8097de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30586-
dc.description.abstractThe current boom of learned query optimizers (LQO) can be explained not only by the general continuous improvement of deep learning (DL) methods but also by the straightforward formulation of a query optimization problem (QOP) as a machine learning (ML) one. The idea is often to replace dynamic programming approaches, widespread for solving QOP, with more powerful methods such as reinforcement learning. However, such a rapid "game change" in the field of QOP could not pass without consequences - other parts of the ML pipeline, except for predictive model development, have large improvement potential. For instance, different LQOs introduce their own restrictions on training data generation from queries, use an arbitrary train/validation approach, and evaluate on a voluntary split of benchmark queries. In this paper, we attempt to standardize the ML pipeline for evaluating LQOs by introducing a new end-to-end benchmarking framework. Additionally, we guide the reader through each data science stage in the ML pipeline and provide novel insights from the machine learning perspective, considering the specifics of QOP. Finally, we perform a rigorous evaluation of existing LQOs, showing that PostgreSQL outperforms these LQOs in almost all experiments depending on the train/test splits.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computing Machineryde_CH
dc.relation.ispartofProceedings of the VLDB Endowmentde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectDatabasede_CH
dc.subjectQuery optimizationde_CH
dc.subjectMachine learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleIs your learned query optimizer behaving as you expect? : a machine learning perspectivede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.14778/3654621.3654625de_CH
dc.identifier.doi10.21256/zhaw-30586-
zhaw.funding.euNode_CH
zhaw.issue7de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1577de_CH
zhaw.pages.start1565de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume17de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf1921052de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedIntelligent Information Systemsde_CH
zhaw.funding.zhawGraphQueryML – Verwendung von maschinellem Lernen zur Optimierung von Abfragen in Graphdatenbanken (SNF/DFG)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Lehmann, C., Sulimov, P., & Stockinger, K. (2024). Is your learned query optimizer behaving as you expect? : a machine learning perspective. Proceedings of the VLDB Endowment, 17(7), 1565–1577. https://doi.org/10.14778/3654621.3654625
Lehmann, C., Sulimov, P. and Stockinger, K. (2024) ‘Is your learned query optimizer behaving as you expect? : a machine learning perspective’, Proceedings of the VLDB Endowment, 17(7), pp. 1565–1577. Available at: https://doi.org/10.14778/3654621.3654625.
C. Lehmann, P. Sulimov, and K. Stockinger, “Is your learned query optimizer behaving as you expect? : a machine learning perspective,” Proceedings of the VLDB Endowment, vol. 17, no. 7, pp. 1565–1577, Mar. 2024, doi: 10.14778/3654621.3654625.
LEHMANN, Claude, Pavel SULIMOV und Kurt STOCKINGER, 2024. Is your learned query optimizer behaving as you expect? : a machine learning perspective. Proceedings of the VLDB Endowment. März 2024. Bd. 17, Nr. 7, S. 1565–1577. DOI 10.14778/3654621.3654625
Lehmann, Claude, Pavel Sulimov, and Kurt Stockinger. 2024. “Is Your Learned Query Optimizer Behaving as You Expect? : A Machine Learning Perspective.” Proceedings of the VLDB Endowment 17 (7): 1565–77. https://doi.org/10.14778/3654621.3654625.
Lehmann, Claude, et al. “Is Your Learned Query Optimizer Behaving as You Expect? : A Machine Learning Perspective.” Proceedings of the VLDB Endowment, vol. 17, no. 7, Mar. 2024, pp. 1565–77, https://doi.org/10.14778/3654621.3654625.


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