Efficient Query Repair for Aggregate Constraints

2025-11-02

AI Summary

This work addresses the problem of rewriting database queries to ensure their results satisfy domain-specific aggregate constraints—such as minimum gender ratio requirements. The proposed method automatically repairs filtering predicates via query rewriting, with a core innovation in modeling aggregate constraints as arithmetic expressions and designing an efficient pruning strategy based on candidate solution set boundary analysis and interval arithmetic. This significantly reduces the search space compared to brute-force enumeration or heuristic baselines. Empirically, the approach achieves 10×–100× speedup in repair time while guaranteeing strict constraint satisfaction. Extensive experiments across diverse real-world datasets and constraint scenarios demonstrate its effectiveness, robustness, and scalability. The method provides a formally verifiable, automated solution for controlled query optimization—offering correctness guarantees unattainable with existing heuristic approaches.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Heuristic Search
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• Repairing queries to satisfy aggregate constraints
• Modifying filter predicates to fulfill domain requirements
• Efficiently pruning search space using interval arithmetic

Innovation

• Repairs queries by modifying filter predicates
• Uses bounds and interval arithmetic pruning
• Efficiently handles aggregate constraint violations