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Zhengchun Liu on Muck Rack

Zhengchun Liu

As seen in: MDPI, Newswise, Amazon Science
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Articles

Intelligent scaling in Amazon Redshift

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By Vikram Nathan, Vikramank Singh, Zhengchun Liu, Mohammad Arifur Rahman
| Amazon Science
Cloud-based data warehouses are built to be easy to use, requiring minimal intervention from customers as their work- loads scale. However, there are still many dimensions of a workload that they do not scale with automatically. For example, in cloud-managed clusters, large ad-hoc queries and ETL workloads must use the same cluster size provisioned for the rest of the workload, and warehouse size does not automatically grow as the underlying data grows in size, causing queries to slow down.
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Stage: Query execution time prediction in Amazon Redshift

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By Ziniu Wu, Ryan Marcus, Zhengchun Liu
| Amazon Science
Query performance (e.g., execution time) prediction is a critical component of modern DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate execution time prediction for many downstream tasks, ranging from high-level optimizations, such as automatically creating materialized views, to low-level tasks on the critical path of query execution, such as admission, scheduling, and execution resource control.
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Evapotranspiration of Winter Wheat in the Semi-Arid Southeastern Loess Plateau Based on Multi-Source Satellite Data

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By Peng He, Rutian Bi, Zhengchun Liu, Lishuai Xu
| MDPI
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
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