Provenance Tracking for End-to-End Machine Learning Pipelines

Stefan Grafberger; Paul Groth; Sebastian Schelter

doi:10.1145/3543873.3587557

Provenance Tracking for End-to-End Machine Learning Pipelines

Stefan Grafberger, Paul Groth, Sebastian Schelter

Elsevier Labs

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Original language	English
Title of host publication	ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
Publisher	Association for Computing Machinery, Inc
Pages	1512
Number of pages	1
ISBN (Electronic)	9781450394161
DOIs	https://doi.org/10.1145/3543873.3587557
State	Published - Apr 30 2023
Event	32nd Companion of the ACM World Wide Web Conference, WWW 2023 - Austin, United States Duration: Apr 30 2023 → May 4 2023

Publication series

Name	ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023

Conference

Conference	32nd Companion of the ACM World Wide Web Conference, WWW 2023
Country/Territory	United States
City	Austin
Period	04/30/23 → 05/4/23

Access to Document

10.1145/3543873.3587557

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Grafberger, S., Groth, P., & Schelter, S. (2023). Provenance Tracking for End-to-End Machine Learning Pipelines. In ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023 (pp. 1512). (ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023). Association for Computing Machinery, Inc. https://doi.org/10.1145/3543873.3587557

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booktitle = "ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023",

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Grafberger, S, Groth, P & Schelter, S 2023, Provenance Tracking for End-to-End Machine Learning Pipelines. in ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023. ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023, Association for Computing Machinery, Inc, pp. 1512, 32nd Companion of the ACM World Wide Web Conference, WWW 2023, Austin, United States, 04/30/23. https://doi.org/10.1145/3543873.3587557

Provenance Tracking for End-to-End Machine Learning Pipelines. / Grafberger, Stefan; Groth, Paul; Schelter, Sebastian.
ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023. Association for Computing Machinery, Inc, 2023. p. 1512 (ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Grafberger, Stefan

AU - Groth, Paul

AU - Schelter, Sebastian

PY - 2023/4/30

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M3 - Contribución a la conferencia

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BT - ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023

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T2 - 32nd Companion of the ACM World Wide Web Conference, WWW 2023

Y2 - 30 April 2023 through 4 May 2023

ER -

Provenance Tracking for End-to-End Machine Learning Pipelines

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