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dc.contributor.authorNegi, Parimarjan
dc.contributor.authorInterlandi, Matteo
dc.contributor.authorMarcus, Ryan
dc.contributor.authorAlizadeh, Mohammad
dc.contributor.authorKraska, Tim
dc.contributor.authorFriedman, Marc
dc.contributor.authorJindal, Alekh
dc.date.accessioned2022-05-25T16:13:28Z
dc.date.available2022-05-25T16:13:28Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/142724
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionof10.1145/3448016.3457568en_US
dc.rightsCreative Commons Attribution 4.0 International Licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.sourceACMen_US
dc.titleSteering Query Optimizers: A Practical Take on Big Data Workloadsen_US
dc.typeArticleen_US
dc.identifier.citationNegi, Parimarjan, Interlandi, Matteo, Marcus, Ryan, Alizadeh, Mohammad, Kraska, Tim et al. 2021. "Steering Query Optimizers: A Practical Take on Big Data Workloads." Proceedings of the 2021 International Conference on Management of Data.
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.relation.journalProceedings of the 2021 International Conference on Management of Dataen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-05-25T16:05:47Z
dspace.orderedauthorsNegi, P; Interlandi, M; Marcus, R; Alizadeh, M; Kraska, T; Friedman, M; Jindal, Aen_US
dspace.date.submission2022-05-25T16:05:48Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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