An Intermediate Representation for Hybrid Database and Machine Learning Workloads
Summary: IFAQ: an IR and compiler for hybrid DB/ML workloads, expressed as iterative programs with functional aggregates. Demonstrates OLAP, linear algebra, and factorization-machine learning on training data from relational feature-extraction queries. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Amir Shaikhha (University of Edinburgh)
- 2. Maximilian Schleich (University of Washington)
- 3. Dan Olteanu (University of Zurich)
BibTeX Citation
@article{shaikhha_vldb21,
title = {{An Intermediate Representation for Hybrid Database and Machine Learning Workloads}},
author = {Shaikhha, Amir and Schleich, Maximilian and Olteanu, Dan},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {2831--2834},
doi = {10.14778/3476311.3476356},
url = {https://doi.org/10.14778/3476311.3476356},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,414 | Optimizing Tensor Programs on Flexible Storage | 2023 | SIGMOD | 6.2258658e-05 |
| 7,904 | Tight Bounds of Circuits for Sum-Product Queries | 2024 | PODS | 5.5181056e-05 |
| 11,481 | Demonstration of OpenDBML, a Framework for Democratizing In-Database Machine Learning | 2023 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 23 | Efficiently Compiling Efficient Query Plans for Modern Hardware | 2011 | VLDB | 0.00054886415 |
| 1,797 | How to Architect a Query Compiler | 2016 | SIGMOD | 9.7368925e-05 |
| 2,769 | A Layered Aggregate Engine for Analytics Workloads | 2019 | SIGMOD | 8.1465406e-05 |
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