Stage: Query Execution Time Prediction in Amazon Redshift-Reference-Cited by-同舟云学术

Stage: Query Execution Time Prediction in Amazon Redshift

Published:2024-06-09 Issue: Volume: Page:
ISSN:
Container-title:Companion of the 2024 International Conference on Management of Data
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Author:

Wu Ziniu¹^ORCID,Marcus Ryan²^ORCID,Liu Zhengchun³^ORCID,Negi Parimarjan¹^ORCID,Nathan Vikram⁴^ORCID,Pfeil Pascal⁵^ORCID,Saxena Gaurav⁶^ORCID,Rahman Mohammad⁶^ORCID,Narayanaswamy Balakrishnan³^ORCID,Kraska Tim⁷^ORCID

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, USA

2. University of Pennsylvania, Philadelphia, USA

3. Amazon Web Services, Santa Clara, USA

4. Amazon Web Services, Boston, USA

5. Amazon Web Services, Munich, Germany

6. Amazon Web Services, East Palo Alto, USA

7. Massachusetts Institute of Technology & Amazon Web Services, Cambridge, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626246.3653391

Reference69 articles.

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2. Automatic Database Management System Tuning Through Large-scale Machine Learning

3. Enhancing one-class support vector machines for unsupervised anomaly detection

4. Bahareh Sadat Arab and Boris Glavic. 2017. Answering Historical What-if Queries with Provenance, Reenactment, and Symbolic Execution. In 9th USENIXWorkshop on the Theory and Practice of Provenance, TaPP 2017, Seattle, WA, USA, June 23, 2017, Adam Bates and Bill Howe (Eds.). USENIX Association. https://www. usenix.org/conference/tapp17/workshop-program/presentation/arab

5. Amazon Redshift Re-invented

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