DIAMetrics: Benchmarking Query Engines at Scale
Summary: DIAMetrics: benchmarking for query engines with summarization, anonymization, execution, monitoring, regression detection, and alerting. Modular, driver-based design abstracts system specifics via canonical formats, enabling scalable benchmarking across engines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shaleen Deep (University of Wisconsin)
- 2. Anja Gruenheid (Google)
- 3. Kruthi Nagaraj (Google)
- 4. Hiro Naito (Google)
- 5. Jeff Naughton (Google)
- 6. Stratis Viglas (Google)
BibTeX Citation
@article{deep_vldb20,
title = {{DIAMetrics: Benchmarking Query Engines at Scale}},
author = {Deep, Shaleen and Gruenheid, Anja and Nagaraj, Kruthi and Naito, Hiro and Naughton, Jeff and Viglas, Stratis},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {3285--3298},
doi = {10.14778/3415478.3415551},
url = {https://doi.org/10.14778/3415478.3415551},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
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