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Predictive Query Language for Relational Databases
This episode explores Predictive Query Language (PQL), a SQL-shaped domain-specific language for defining supervised learning tasks directly over relational databases by specifying the prediction target, entity, and future time horizon in one declarative statement. It explains why training label generation is often the real bottleneck in applied machine learning, unpacking concepts like prediction entities, relational learning, anchor times, point-in-time consistency, and information leakage. The discussion compares PQL to earlier work on prediction engineering and relational deep learning, arguing that its main contribution is not a new model but a more disciplined way to construct temporally valid prediction problems from messy, multi-table operational data. Listeners interested in real-world ML systems will find it interesting because it focuses on the part most papers skip: how to ask the predictive question correctly when database history is incomplete, revised, and easy to misuse.
Sources:
1. Predictive Query Language for Relational Databases
https://arxiv.org/pdf/2602.09572
2. Declarative Machine Learning - A Classification of Basic Properties and Types — Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald, 2016
https://arxiv.org/abs/1605.05826
3. The MADlib Analytics Library or MAD Skills, the SQL — Joseph M. Hellerstein, Christopher Re, Florian Schoppmann, Daisy Zhe Wang, et al., 2012
https://arxiv.org/abs/1208.4165
4. MLog: Towards Declarative In-Database Machine Learning — Xupeng Li, Bin Cui, Yiru Chen, Wentao Wu, Ce Zhang, 2017
https://www.vldb.org/pvldb/vol10/p1933-zhang.pdf
5. sql4ml A declarative end-to-end workflow for machine learning — Nantia Makrynioti, Ruy Ley-Wild, Vasilis Vassalos, 2019
https://arxiv.org/abs/1907.12415
6. Deep Feature Synthesis: Towards Automating Data Science Endeavors — James Max Kanter, Kalyan Veeramachaneni, 2015
https://doi.org/10.1109/DSAA.2015.7344858
7. Label, Segment, Featurize: A Cross Domain Framework for Prediction Engineering — James Max Kanter, Owen Gillespie, Kalyan Veeramachaneni, 2016
https://www.maxkanter.com/papers/DSAA_LSF_2016.pdf
8. Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases — Vid Kocijan, Jinu Sunil, Jan Eric Lenssen, Viman Deb, et al., 2026
https://arxiv.org/abs/2602.09572
9. RelBench: A Benchmark for Deep Learning on Relational Databases — Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, et al., 2024
https://arxiv.org/abs/2407.20060
10. Temporal features in SQL:2011 — Krishna Kulkarni, Jan-Eike Michels, 2012
https://sigmodrecord.org/publications/sigmodRecord/1209/pdfs/07.industry.kulkarni.pdf
11. Time Travel and Provenance for Machine Learning Pipelines — Alexandru A. Ormenisan, Moritz Meister, Fabio Buso, Robin Andersson, Seif Haridi, Jim Dowling, 2020
https://www.usenix.org/conference/opml20/presentation/ormenisan
12. Optimizing Data Pipelines for Machine Learning in Feature Stores — Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, et al., 2023
https://www.microsoft.com/en-us/research/publication/optimizing-data-pipelines-for-machine-learning-in-feature-stores/
13. The Hopsworks Feature Store for Machine Learning — Javier de la Rua Martinez, Fabio Buso, Antonios Kouzoupis, Alexandru A. Ormenisan, et al., 2024
https://content.hopsworks.ai/hubfs/The_Hopsworks_Feature_Store_for_Machine_Learning.pdf
14. Leakage in Data Mining: Formulation, Detection, and Avoidance — Shachar Kaufman, Saharon Rosset, Claudia Perlich, 2012
https://doi.org/10.1145/2020408.2020496
15. How to avoid machine learning pitfalls: a guide for academic researchers — Michael A. Lones, 2021
https://arxiv.org/abs/2108.02497
16. Leakage and the reproducibility crisis in machine-learning-based science — Sayash Kapoor, Arvind Narayanan, 2023
https://doi.org/10.1016/j.patter.2023.100804
17. MLearn: A Declarative Machine Learning Language for Database Systems — Maximilian E. Schuele, Matthias Bungeroth, Alfons Kemper, Stephan Guennemann, Thomas Neumann, 2019
https://scholar.google.com/scholar?q=MLearn%3A+A+Declarative+Machine+Learning+Language+for+Database+Systems
18. End-to-end Optimization of Machine Learning Prediction Queries — Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, 2022
https://scholar.google.com/scholar?q=End-to-end+Optimization+of+Machine+Learning+Prediction+Queries
19. Relational Deep Learning: Graph Representation Learning on Relational Databases — Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec, 2023
https://scholar.google.com/scholar?q=Relational+Deep+Learning%3A+Graph+Representation+Learning+on+Relational+Databases
20. KumoRFM: A Foundation Model for In-Context Learning on Relational Data — Matthias Fey, Vid Kocijan, Federico Lopez, Jan Eric Lenssen, Jure Leskovec, 2025
https://scholar.google.com/scholar?q=KumoRFM%3A+A+Foundation+Model+for+In-Context+Learning+on+Relational+Data
21. Toward a Declarative Query Language for Machine Learning — Hasan M. Jamil, 2024
https://vldb.org/workshops/2024/proceedings/TaDA/TaDA.2.pdf
22. Implementing a Declarative Query Language for High Level Machine Learning Application Design — Hasan Rahman, Hasan M. Jamil, 2025
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5210204
23. LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL — Yihan Wang, Peiyu Liu, Xin Yang, 2025
https://arxiv.org/abs/2503.18596
24. E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL — Hasan Alp Caferoglu, Ozgur Ulusoy, 2024
https://arxiv.org/abs/2409.16751
25. AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale — Ziyang Wang et al., 2025
https://arxiv.org/abs/2511.17190
26. From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment — Yu Zhao et al., 2023
https://arxiv.org/abs/2305.11501
27. AI Post Transformers: SGLang for Faster Structured LLM Programs — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-06-sglang-for-faster-structured-llm-program-c59f1c.mp3
28. AI Post Transformers: Caffe and the Rise of CNN Frameworks — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-06-caffe-and-the-rise-of-cnn-frameworks-cf15f3.mp3
View all episodes
By mcgrofThis episode explores Predictive Query Language (PQL), a SQL-shaped domain-specific language for defining supervised learning tasks directly over relational databases by specifying the prediction target, entity, and future time horizon in one declarative statement. It explains why training label generation is often the real bottleneck in applied machine learning, unpacking concepts like prediction entities, relational learning, anchor times, point-in-time consistency, and information leakage. The discussion compares PQL to earlier work on prediction engineering and relational deep learning, arguing that its main contribution is not a new model but a more disciplined way to construct temporally valid prediction problems from messy, multi-table operational data. Listeners interested in real-world ML systems will find it interesting because it focuses on the part most papers skip: how to ask the predictive question correctly when database history is incomplete, revised, and easy to misuse.
Sources:
1. Predictive Query Language for Relational Databases
https://arxiv.org/pdf/2602.09572
2. Declarative Machine Learning - A Classification of Basic Properties and Types — Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald, 2016
https://arxiv.org/abs/1605.05826
3. The MADlib Analytics Library or MAD Skills, the SQL — Joseph M. Hellerstein, Christopher Re, Florian Schoppmann, Daisy Zhe Wang, et al., 2012
https://arxiv.org/abs/1208.4165
4. MLog: Towards Declarative In-Database Machine Learning — Xupeng Li, Bin Cui, Yiru Chen, Wentao Wu, Ce Zhang, 2017
https://www.vldb.org/pvldb/vol10/p1933-zhang.pdf
5. sql4ml A declarative end-to-end workflow for machine learning — Nantia Makrynioti, Ruy Ley-Wild, Vasilis Vassalos, 2019
https://arxiv.org/abs/1907.12415
6. Deep Feature Synthesis: Towards Automating Data Science Endeavors — James Max Kanter, Kalyan Veeramachaneni, 2015
https://doi.org/10.1109/DSAA.2015.7344858
7. Label, Segment, Featurize: A Cross Domain Framework for Prediction Engineering — James Max Kanter, Owen Gillespie, Kalyan Veeramachaneni, 2016
https://www.maxkanter.com/papers/DSAA_LSF_2016.pdf
8. Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases — Vid Kocijan, Jinu Sunil, Jan Eric Lenssen, Viman Deb, et al., 2026
https://arxiv.org/abs/2602.09572
9. RelBench: A Benchmark for Deep Learning on Relational Databases — Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, et al., 2024
https://arxiv.org/abs/2407.20060
10. Temporal features in SQL:2011 — Krishna Kulkarni, Jan-Eike Michels, 2012
https://sigmodrecord.org/publications/sigmodRecord/1209/pdfs/07.industry.kulkarni.pdf
11. Time Travel and Provenance for Machine Learning Pipelines — Alexandru A. Ormenisan, Moritz Meister, Fabio Buso, Robin Andersson, Seif Haridi, Jim Dowling, 2020
https://www.usenix.org/conference/opml20/presentation/ormenisan
12. Optimizing Data Pipelines for Machine Learning in Feature Stores — Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, et al., 2023
https://www.microsoft.com/en-us/research/publication/optimizing-data-pipelines-for-machine-learning-in-feature-stores/
13. The Hopsworks Feature Store for Machine Learning — Javier de la Rua Martinez, Fabio Buso, Antonios Kouzoupis, Alexandru A. Ormenisan, et al., 2024
https://content.hopsworks.ai/hubfs/The_Hopsworks_Feature_Store_for_Machine_Learning.pdf
14. Leakage in Data Mining: Formulation, Detection, and Avoidance — Shachar Kaufman, Saharon Rosset, Claudia Perlich, 2012
https://doi.org/10.1145/2020408.2020496
15. How to avoid machine learning pitfalls: a guide for academic researchers — Michael A. Lones, 2021
https://arxiv.org/abs/2108.02497
16. Leakage and the reproducibility crisis in machine-learning-based science — Sayash Kapoor, Arvind Narayanan, 2023
https://doi.org/10.1016/j.patter.2023.100804
17. MLearn: A Declarative Machine Learning Language for Database Systems — Maximilian E. Schuele, Matthias Bungeroth, Alfons Kemper, Stephan Guennemann, Thomas Neumann, 2019
https://scholar.google.com/scholar?q=MLearn%3A+A+Declarative+Machine+Learning+Language+for+Database+Systems
18. End-to-end Optimization of Machine Learning Prediction Queries — Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, 2022
https://scholar.google.com/scholar?q=End-to-end+Optimization+of+Machine+Learning+Prediction+Queries
19. Relational Deep Learning: Graph Representation Learning on Relational Databases — Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec, 2023
https://scholar.google.com/scholar?q=Relational+Deep+Learning%3A+Graph+Representation+Learning+on+Relational+Databases
20. KumoRFM: A Foundation Model for In-Context Learning on Relational Data — Matthias Fey, Vid Kocijan, Federico Lopez, Jan Eric Lenssen, Jure Leskovec, 2025
https://scholar.google.com/scholar?q=KumoRFM%3A+A+Foundation+Model+for+In-Context+Learning+on+Relational+Data
21. Toward a Declarative Query Language for Machine Learning — Hasan M. Jamil, 2024
https://vldb.org/workshops/2024/proceedings/TaDA/TaDA.2.pdf
22. Implementing a Declarative Query Language for High Level Machine Learning Application Design — Hasan Rahman, Hasan M. Jamil, 2025
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5210204
23. LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL — Yihan Wang, Peiyu Liu, Xin Yang, 2025
https://arxiv.org/abs/2503.18596
24. E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL — Hasan Alp Caferoglu, Ozgur Ulusoy, 2024
https://arxiv.org/abs/2409.16751
25. AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale — Ziyang Wang et al., 2025
https://arxiv.org/abs/2511.17190
26. From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment — Yu Zhao et al., 2023
https://arxiv.org/abs/2305.11501
27. AI Post Transformers: SGLang for Faster Structured LLM Programs — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-06-sglang-for-faster-structured-llm-program-c59f1c.mp3
28. AI Post Transformers: Caffe and the Rise of CNN Frameworks — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-06-caffe-and-the-rise-of-cnn-frameworks-cf15f3.mp3