P2D: A Transpiler Framework for Optimizing Data Science Pipelines-Reference-Cited by-同舟云学术

P2D: A Transpiler Framework for Optimizing Data Science Pipelines

Published:2023-06-18 Issue: Volume: Page:
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Container-title:Proceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning
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Author:

Grigorov Yordan¹^ORCID,Gavriilidis Haralampos²^ORCID,Redyuk Sergey³^ORCID,Beedkar Kaustubh⁴^ORCID,Markl Volker²^ORCID

Affiliation:

1. Cisco Systems, Sofia, BG

2. Technische Universität Berlin, Berlin, DE

3. DFKI GmbH, Berlin, DE

4. Indian Institute of Technology Delhi, New Delhi, IN

Funder

German Ministry for Education & Research

Publisher

ACM

Reference23 articles.

1. 2023. Ibis. https://github.com/ibis-project/ibis 2023. Ibis. https://github.com/ibis-project/ibis

2. 2023. Koalas. https://github.com/databricks/koalas 2023. Koalas. https://github.com/databricks/koalas

3. Alexander Alexandrov et al. 2015. Implicit parallelism through deep language embedding. In SIGMOD. Alexander Alexandrov et al. 2015. Implicit parallelism through deep language embedding. In SIGMOD.

4. Haralampos Gavriilidis et al. 2022. Towards a Modular Data Management System Framework. In CDMS. Haralampos Gavriilidis et al. 2022. Towards a Modular Data Management System Framework. In CDMS.

5. Haralampos Gavriilidis et al. 2023. In-situ cross-database query processing. In ICDE. Haralampos Gavriilidis et al. 2023. In-situ cross-database query processing. In ICDE.

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