Publication:

Reproducibility Report for ACM SIGMOD 2024 Paper: 'StarfishDB: A Query Execution Engine for Relational Probabilistic Programming'

cris.virtual.department

DIAS

cris.virtual.parent-organization

IINFCOM

cris.virtual.parent-organization

IC

cris.virtual.parent-organization

EPFL

cris.virtual.sciperId

387265

cris.virtual.unitId

11836

cris.virtual.unitManager

Ailamaki, Anastasia

cris.virtualsource.author-scopus

733da83a-a524-48e0-9adc-aee74c7383dc

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733da83a-a524-48e0-9adc-aee74c7383dc

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733da83a-a524-48e0-9adc-aee74c7383dc

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4ec05286-5e85-43a2-add2-fb9432c5646f

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4ec05286-5e85-43a2-add2-fb9432c5646f

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datacite.rights

metadata-only

dc.contributor.author

Krause, Alexander

dc.contributor.author

Lebedev, Georgiy

dc.contributor.author

Hadouaj, Sami

dc.contributor.author

Ben Amara, Ouael

dc.contributor.author

Meneghetti, Niccolò

dc.date.accessioned

2025-04-16T08:52:47Z

dc.date.available

2025-04-16T08:52:47Z

dc.date.created

2025-04-14

dc.date.issued

2025-03-21

dc.date.modified

2025-04-16T08:53:40.728994Z

dc.description.abstract

The core thesis of this paper could easily be reproduced in the original environment. The authors provided an almost fully automated set of scripts that automatically execute the experiments. The experimental data was automatically collected and plotted. The resulting figures have some minor deviations from the originals, but the overall trends remain the same, and hence they can be considered a good fit.

en
dc.description.sponsorship

DIAS

dc.identifier.doi

10.1145/3687998.3717056

dc.identifier.uri

https://infoscience.epfl.ch/handle/20.500.14299/249341

dc.language.iso

en

dc.publisher

ACM

dc.publisher.place

New York, NY, USA

dc.relation.conference

SIGMOD/PODS '24: International Conference on Management of Data

dc.relation.doi

https://doi.org/10.1145/3687998.3717056

dc.relation.isbn

979-8-4007-1179-4

dc.relation.ispartof

SIGMOD/PODS '24: Reproducibility Reports of the 2024 International Conference on Management of Data

dc.title

Reproducibility Report for ACM SIGMOD 2024 Paper: 'StarfishDB: A Query Execution Engine for Relational Probabilistic Programming'

dc.type

text::conference output::conference proceedings::conference paper

en
dspace.entity.type

Publication

epfl.peerreviewed

REVIEWED

epfl.relation.conferenceType

conference

epfl.workflow.startDateTime

2025-04-14T09:52:07.090Z

epfl.writtenAt

EPFL

oaire.citation.conferenceDate

2024-06-09 - 2024-06-15

oaire.citation.conferencePlace

Santiago AA Chile

oaire.citation.endPage

31

oaire.citation.startPage

28

oairecerif.acronym

SIGMOD/PODS '24

oairecerif.author.affiliation

Technische Universität Dresden, Dresden, Germany

oairecerif.author.affiliation

EPFL

oairecerif.author.affiliation

University of Michigan-Dearborn, Dearborn, MI, USA

oairecerif.author.affiliation

University of Michigan-Dearborn, Dearborn, MI, USA

oairecerif.author.affiliation

University of Michigan-Dearborn, Dearborn, MI, USA

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