Object-relational mapping (ORM) frameworks simplify persistence, yet routine mapping choices—including inheritance strategy, association encoding, and denormalization—can substantially alter
the generated SQL and the trade-offs among query latency, insert/update cost, and storage footprint. Existing optimization approaches either guarantee semantic validity but incur expensive
per-candidate deployment and benchmarking, or learn from schema structure alone and therefore miss the fact that workload behavior is mapping-dependent. We present TriORM, a workload-aware neural symbolic framework for multi-objective ORM mapping design that removes per-candidateworkload execution from the online recommendation loop while preserving validity by construction. TriORM (i) enumerates admissible mappings via bounded relational synthesis in Alloy, (ii) concretizes an abstract workload into schema-specific SQL templates for each candidate, and (iii) predicts continuous objectives using a tri-input model that fuses a typed schema-graph encoder, a concretized-workload encoder, and interpretable static cost proxies. The resulting predictions enable Pareto filtering and user-weighted selection without deploying or executing each candidate at inference time; profiling is performed only offline to obtain supervision. On nine TradeMaker/Leant benchmark
models, TriORM improves mean Pareto-front approximation
over Leant (GD/HV 0.03/0.78 vs. 0.07/0.61) and reduces end-toend
recommendation time (3.6×103s vs. 2.6×104s), while preserving
semantic correctness within the chosen synthesis bounds.