Maintaining Queries under Updates Using Heavy-Light Partitioning of the Input Relations

2026-05-08

AI Summary

This work addresses the problem of efficiently maintaining join query results under tuple-level updates to support constant-delay enumeration. The authors propose a general-purpose incremental maintenance framework that integrates incremental query evaluation, materialized view trees, and a heavy–light data partitioning strategy. A key contribution is the introduction of a novel metric, “maintenance width,” which guides the selection of an optimal heavy–light threshold. The approach applies to arbitrary join queries and achieves update times that match or improve upon the current state of the art, substantially broadening the scope of queries amenable to efficient incremental maintenance.

Abstract

We study the classical incremental view maintenance problem: Given a query and a database, maintain the query output under single-tuple updates (inserts or deletes) to the database such that the tuples in the query output can be enumerated with constant delay after any update. We introduce a maintenance approach whose update time matches or improves the best update time reported in prior work. Whereas prior approaches are manually tailored to each of a handful of queries, our approach generalizes to arbitrary join queries. It combines three techniques: delta queries, trees of materialized views, and heavy-light data partitioning. The overall update time incurred by our approach for a given join query is characterized by the maintenance width, a new measure that is parameterized by the heavy-light threshold for data partitioning. We show how to find the threshold that minimizes the maintenance width.

Problem

• incremental view maintenance
• join queries
• constant delay enumeration
• single-tuple updates
• query output maintenance

Innovation

• incremental view maintenance
• heavy-light partitioning
• maintenance width
• delta queries
• materialized views