Efficient Query Repair for Aggregate Constraints

TL;DR

This paper presents a novel method for repairing database queries to satisfy aggregate constraints by modifying filter predicates, using bounds and interval arithmetic for efficient pruning, outperforming existing approaches.

Contribution

The paper introduces a new query repair technique leveraging bounds and interval arithmetic to efficiently ensure aggregate constraints are met.

Findings

Significantly outperforms baseline methods in experimental evaluations.

Efficient pruning reduces computational complexity.

Applicable to real-world scenarios with domain-specific aggregate constraints.

Abstract

In many real-world scenarios, query results must satisfy domain-specific constraints. For instance, a minimum percentage of interview candidates selected based on their qualifications should be female. These requirements can be expressed as constraints over an arithmetic combination of aggregates evaluated on the result of the query. In this work, we study how to repair a query to fulfill such constraints by modifying the filter predicates of the query. We introduce a novel query repair technique that leverages bounds on sets of candidate solutions and interval arithmetic to efficiently prune the search space. We demonstrate experimentally, that our technique significantly outperforms baselines that consider a single candidate at a time.