TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems
Summary: TPCx-AI: the first standardized, industry benchmark for end-to-end ML deployments. Uniquely combines scalable realistic structured/unstructured data generation with representative integration, processing, training, and inference workloads across Python and Spark. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Christoph Brücke (bankmark)
- 2. Philipp Härtling (bankmark)
- 3. Hamesh Patel (Intel)
- 4. Rodrigo D Escobar Palacios (Intel)
- 5. Tilmann Rabl (Hasso Plattner Institute; University of Potsdam; bankmark)
BibTeX Citation
@article{brucke_vldb23,
title = {{TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems}},
author = {Brücke, Christoph and Härtling, Philipp and Patel, Hamesh and Palacios, Rodrigo D Escobar and Rabl, Tilmann},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3649--3661},
doi = {10.14778/3611540.3611554},
url = {https://doi.org/10.14778/3611540.3611554},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 479 | The Making of TPC-DS | 2006 | VLDB | 0.00017622471 |
| 1,153 | Data Management Challenges in Production Machine Learning | 2017 | SIGMOD | 0.00011798912 |
| 1,702 | BigBench: Towards an Industry Standard Benchmark for Big Data Analytics | 2013 | SIGMOD | 9.8361594e-05 |
| 2,187 | Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities | 2021 | SIGMOD | 8.8896655e-05 |
| 3,518 | A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics | 2018 | VLDB | 7.2400627e-05 |
| 4,929 | TPC-DI: The First Industry Benchmark for Data Integration | 2014 | VLDB | 6.3494846e-05 |
| 7,888 | MLBench: Benchmarking Machine Learning Services Against Human Experts | 2018 | VLDB | 5.4330038e-05 |
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