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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)

Paper ID
12397
Venue
VLDB
Year
2020
Pagerank
7.3939262e-05
Overall Rank
3,460 | 76.27%
DOI
10.14778/3415478.3415551

Incoming Non-self Citations Over Time

Authors

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}
}

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