Neural-Symbolic Multi-objective Optimization for Performance-Aware ORM Database Design

Abstract

Object-relational mapping (ORM) design remains largely driven by fixed heuristics that fail to capture workload-specific tradeoffs among query latency, insert cost, and memory footprint. We present Y-Map, a hybrid neural--symbolic framework for performance-aware ORM schema design that synthesizes valid schema candidates and predicts their performance without requiring workload execution at inference time. Y-Map leverages Alloy to enumerate correctness-preserving ORM schemas and ranks them using a multi-encoder regression model that fuses structural, syntactic, and semantic representations with compact schema-level features. By predicting continuous performance objectives---insert latency, query latency, and memory footprint---Y-Map enables Pareto-aware selection without per-candidate benchmarking during inference. We evaluate Y-Map on nine object models from e-commerce, banking, and healthcare. Relative to two representative baselines, Leant and DTS, Y-Map yields improved aggregate Pareto quality (Generational Distance and Hypervolume) while reducing inference time and memory overhead. The experimental results show that integrating symbolic validity guarantees with learned performance prediction provides a practical, scalable solution for workload-aware ORM optimization.