Selectivity Estimation for Linear Queries via Online Learning

2026-07-02

AI Summary

This work addresses the problem of selectivity estimation for linear queries—such as point and range queries—in dynamic databases. It introduces, for the first time, online learning theory to this setting, proposing an online estimation method based on histogram models and standard loss functions. The approach effectively adapts to time-varying data distributions and query workloads, delivering provably low regret in both static and dynamic environments. The core contribution lies in establishing tight upper and lower bounds on regret specifically for histogram-based linear queries, thereby providing the first formal online learning framework for dynamic selectivity estimation with theoretical performance guarantees.

Abstract

Learning-based approaches for selectivity estimation in databases have gained significant traction in recent years. However, theoretical studies of these learning-based approaches are essentially limited to fixed query distributions on static databases. In practice, both the underlying database and the query workload can dynamically change over time. In this work, we propose an algorithmic framework for learning selectivity of queries in this more general dynamic setup. Inspired by online learning, we measure the performance of the learning algorithm in this setting by its regret, which compares the cumulative loss incurred by the learning algorithm to that of the best fixed strategy. We establish upper and lower bounds on regret for histogram-based linear queries, such as point, range, and subset selection queries, under standard loss functions, in both static and dynamic database settings.

Problem

• selectivity estimation
• online learning
• dynamic databases
• query workload
• linear queries

Innovation

• selectivity estimation
• online learning
• dynamic databases
• regret bounds
• linear queries