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[2602.09572] Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases
Summary
The paper introduces Predictive Query Language (PQL), a domain-specific language designed to streamline predictive modeling on relational databases, enhancing efficiency in generating training data for machine learning tasks.
Why It Matters
PQL addresses the challenges of manual data extraction for predictive modeling, which is often slow and error-prone. By automating this process, PQL can significantly improve the speed and accuracy of machine learning applications across various domains, making it a valuable tool for data scientists and engineers.
Key Takeaways
- PQL simplifies the definition of predictive tasks with a single declarative query.
- It automates the computation of training labels for diverse machine learning tasks.
- PQL has been successfully integrated into real-world applications, demonstrating its versatility.
Computer Science > Databases arXiv:2602.09572 (cs) [Submitted on 10 Feb 2026 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases Authors:Vid Kocijan, Jinu Sunil, Jan Eric Lenssen, Viman Deb, Xinwei Xe, Federico Reyes Gomez, Matthias Fey, Jure Leskovec View a PDF of the paper titled Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases, by Vid Kocijan and Jinu Sunil and Jan Eric Lenssen and Viman Deb and Xinwei Xe and Federico Reyes Gomez and Matthias Fey and Jure Leskovec View PDF HTML (experimental) Abstract:The purpose of predictive modeling on relational data is to predict future or missing values in a relational database, for example, future purchases of a user, risk of readmission of the patient, or the likelihood that a financial transaction is fraudulent. Typically powered by machine learning methods, predictive models are used in recommendations, financial fraud detection, supply chain optimization, and other systems, providing billions of predictions every day. However, training a machine learning model requires manual work to extract the required training examples - prediction entities and target labels - from the database, which is slow, laborious, and prone to mistakes. Here, we present the Predictive Query Language (PQL), an SQL-inspired declarative language for defining predictive tasks on relational databa...
