Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines-Reference-Cited by-同舟云学术

Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Tu Dezhan¹^ORCID,He Yeye²^ORCID,Cui Weiwei³^ORCID,Ge Song³^ORCID,Zhang Haidong³^ORCID,Han Shi³^ORCID,Zhang Dongmei³^ORCID,Chaudhuri Surajit²^ORCID

Affiliation:

1. University of California, Los Angeles, Los Angeles, CA, USA

2. Microsoft Research, Redmond, WA, USA

3. Microsoft Research, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599776

Reference67 articles.

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2. [n. d.]. Azure ML Drift Detection Service. https://docs.microsoft.com/en-us/azure/machine-learning/howto-monitor-datasets. [n. d.]. Azure ML Drift Detection Service. https://docs.microsoft.com/en-us/azure/machine-learning/howto-monitor-datasets.

3. [n. d.]. Code and data to be released after review at. https://github.com/River12/Auto-Validate-by-History. [n. d.]. Code and data to be released after review at. https://github.com/River12/Auto-Validate-by-History.

4. [n. d.]. Deequ: recommended settings. (Retrieved in 02/2022). https://github.com/awslabs/deequ/tree/master/src/main/scala/com/amazon/deequ/suggestions/rules. [n. d.]. Deequ: recommended settings. (Retrieved in 02/2022). https://github.com/awslabs/deequ/tree/master/src/main/scala/com/amazon/deequ/suggestions/rules.

5. [n. d.]. Full version of the paper: Auto-Validate-by-History. https://arxiv.org/abs/2306.02421. [n. d.]. Full version of the paper: Auto-Validate-by-History. https://arxiv.org/abs/2306.02421.