Materialization and Reuse Optimizations for Production Data Science Pipelines-Reference-Cited by-同舟云学术

Materialization and Reuse Optimizations for Production Data Science Pipelines

Published:2022-06-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 International Conference on Management of Data
language:
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Author:

Derakhshan Behrouz¹,Rezaei Mahdiraji Alireza²,Kaoudi Zoi³,Rabl Tilmann⁴,Markl Volker⁵

Affiliation:

1. DFKI GmbH, Berlin, Germany

2. Yara Digital Production, Berlin, Germany

3. TU Berlin, Berlin, Germany

4. HPI & University of Potsdam, Potsdam, Germany

5. TU Berlin & DFKI GmbH, Berlin, Germany

Funder

German Ministry for Education and Research as BIFOLD - Berlin Institute for the Foundations of Learning and Data

German Federal Ministry for Economic Affairs and Climate Action, Project ?ExDRa?

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3514221.3526186

Reference59 articles.

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2. Pierre Baldi , Peter Sadowski , and Daniel Whiteson . 2014. Searching for exotic particles in high-energy physics with deep learning. Nature communications , Vol. 5 , 1 ( 2014 ), 1--9. Pierre Baldi, Peter Sadowski, and Daniel Whiteson. 2014. Searching for exotic particles in high-energy physics with deep learning. Nature communications, Vol. 5, 1 (2014), 1--9.

3. TFX

4. Principles of dataset versioning

5. Jeffrey Chung . 2020. NYC Taxi Trip - Public. kaggle.com/jeffreycbw/nyc-taxi-trip-public-0--37399-private-0--37206 Retrieved August 23, 2021 from Jeffrey Chung. 2020. NYC Taxi Trip - Public. kaggle.com/jeffreycbw/nyc-taxi-trip-public-0--37399-private-0--37206 Retrieved August 23, 2021 from

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