TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems
Summary: Introduces TPCx-AI, the first industry-standard end-to-end ML benchmark that models real ML pipelines (data integration, processing, training, inference) with both Python and Spark implementations. Novelty: unified structured+unstructured dataset, PB-scale data generator, and diverse representative workloads enabling fair, scalable system comparisons. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,575 | Optimizing Data Pipelines for Machine Learning in Feature Stores | 2023 | VLDB | 5.4253204e-05 |
| 6,377 | Mitigating the Impedance Mismatch between Prediction Query Execution and Database Engine | 2025 | SIGMOD | 5.0860948e-05 |
| 6,898 | Decentralized Actor Scheduling and Reference-based Storage in Xorbits: a Native Scalable Data Science Engine | 2025 | VLDB | 4.8878659e-05 |
| 9,243 | The Hopsworks Feature Store for Machine Learning | 2024 | SIGMOD | 4.3648789e-05 |
| 10,095 | NeurStore: Efficient In-database Deep Learning Model Management System | 2026 | SIGMOD | 4.1905499e-05 |
| 10,243 | TPCx-AI under the Microscope: A Benchmarking Debt Analysis | 2026 | VLDB | 4.1905499e-05 |
| 10,332 | ELT-Bench: An End-to-End Benchmark for Evaluating AI Agents on ELT Pipelines | 2026 | VLDB | 4.1905499e-05 |
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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 |
|---|---|---|---|---|
| 659 | The Making of TPC-DS | 2006 | VLDB | 0.00018514913 |
| 1,422 | Data Management Challenges in Production Machine Learning | 2017 | SIGMOD | 0.00012050431 |
| 1,728 | BigBench: Towards an Industry Standard Benchmark for Big Data Analytics | 2013 | SIGMOD | 0.00010731422 |
| 2,456 | Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities | 2021 | SIGMOD | 8.7649259e-05 |
| 3,953 | A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics | 2018 | VLDB | 6.5896733e-05 |
| 5,115 | TPC-DI: The First Industry Benchmark for Data Integration | 2014 | VLDB | 5.6808821e-05 |
| 7,419 | MLBench: Benchmarking Machine Learning Services Against Human Experts | 2018 | VLDB | 4.7302312e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,800 | Why You Should Run TPC-DS: A Workload Analysis | 2007 | VLDB | 6.7529849e-05 |
| 9,645 | PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking | 2025 | VLDB | 4.3067693e-05 |
| 9,479 | Revisiting Graph Analytics Benchmark | 2025 | SIGMOD | 4.3300131e-05 |
| 1,728 | BigBench: Towards an Industry Standard Benchmark for Big Data Analytics | 2013 | SIGMOD | 0.00010731422 |
| 9,372 | FEBench: A Benchmark for Real-Time Relational Data Feature Extraction | 2023 | VLDB | 4.3461481e-05 |
| 5,115 | TPC-DI: The First Industry Benchmark for Data Integration | 2014 | VLDB | 5.6808821e-05 |
| 3,460 | A Comparison of Platforms for Implementing and Running Very Large Scale Machine Learning Algorithms | 2014 | SIGMOD | 7.0702034e-05 |
| 7,419 | MLBench: Benchmarking Machine Learning Services Against Human Experts | 2018 | VLDB | 4.7302312e-05 |
| 3,260 | Query Processing on Tensor Computation Runtimes | 2022 | VLDB | 7.3091312e-05 |
| 10,243 | TPCx-AI under the Microscope: A Benchmarking Debt Analysis | 2026 | VLDB | 4.1905499e-05 |