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

TL;DR

The paper introduces PQL, a declarative SQL-inspired language that simplifies defining predictive modeling tasks on relational databases, automating training data extraction for various machine learning applications.

Contribution

It presents PQL, a novel language that streamlines predictive task specification and training data generation directly within relational databases, enhancing efficiency and reducing errors.

Findings

PQL enables single-query specification of diverse predictive tasks.

The language is integrated into a predictive AI platform with successful real-world use cases.

Two implementations demonstrate scalability from small to large databases.

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 databases. PQL allows specifying a predictive task in a single declarative query, enabling the automatic computation of 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.