Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases

2026-02-10

AI Summary

This work addresses the cumbersome and error-prone process of manually extracting samples and labels from relational databases for traditional machine learning modeling. To streamline this workflow, the authors propose PQL, a declarative domain-specific language inspired by SQL that enables users to define diverse predictive tasks—including regression, classification, time-series forecasting, and recommendation—through a single query directly over relational databases, with training labels automatically generated. PQL offers two implementations: one optimized for low-latency, small-scale scenarios and another designed for large-scale data processing. The approach has been validated in real-world applications such as financial fraud detection, product recommendation, and load forecasting, demonstrating its versatility, efficiency, and significant improvements in modeling productivity and scalability.

Technology Category

• Machine Learning: Statistical Relational/Logic Learning
• Data Mining & Knowledge Management: Intelligent Query Processing
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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), a SQL-inspired declarative language for defining predictive tasks on relational databases. PQL allows specifying a predictive task in a single declarative query, enabling the automatic computation training labels for a large variety of machine learning tasks, such as regression, classification, time-series forecasting, and recommender systems. PQL is already successfully integrated and used in a collection of use cases as part of a predictive AI platform. The versatility of the language can be demonstrated through its many ongoing use cases, including financial fraud, item recommendations, and workload prediction. We demonstrate its versatile design through two implementations; one for small-scale, low-latency use and one that can handle large-scale databases.

Problem

• predictive modeling
• relational databases
• training data extraction
• machine learning
• declarative language

Innovation

• Predictive Query Language
• declarative language
• relational databases
• automated label generation
• predictive modeling